Description:FIELD OF THE INVENTION
This invention relates to Photographic Dermatological Diagnosis Tool.
BACKGROUND OF THE INVENTION
The skin disease detection system using AI/ML addresses several problems associated with traditional methods of diagnosing skin diseases:
Personalization and Human Error: Dermatologists' subjective visual assessments of skin problems can differ, resulting in inconsistent diagnoses. The method offers an automated, objective evaluation that improves diagnostic precision and lessens the impact of human mistake.
Time-consuming Process: It can take a while to manually examine skin issues, especially when there are several individuals involved. The AI/ML-based technology offers a quicker and more effective method, making it possible to quickly analyses and diagnose skin problems.
Accessibility Issues: In some regions, access to dermatologists may be restricted, delaying diagnosis and treatment. The technology offers a simple solution that may be used in a variety of healthcare settings, increasing access to the detection of skin diseases.
Knowledge and Training: Accurate diagnosis of skin diseases frequently requires specialized training and experience. The AI/ML solution uses extensive training on a variety of datasets to capture dermatological expertise and make it accessible automatically, even in environments where such expertise is limited.
Intervention and early detection: Effective therapies for skin diseases depends on early identification. Early skin problem detection is made possible by the technology, leading to quicker treatment and better patient results.
Cost-Effectiveness: Multiple consultations and tests may be required as part of the conventional diagnostic procedure for skin illnesses, which raises the expense of care. The AI/ML system may be able to decrease the need for pointless consultations and tests by offering an automated and accurate diagnosis, saving patients and healthcare systems money.
202341033619 A system that can analyses photos and notify dermatologists of the existence of melanoma might potentially eliminate the need for a lot of manual diagnosis work. Similar findings to those of the pertained model were produced by our created model. In simulations using the 2017 International Skin Imaging Collaboration skin cancer dataset, the suggested method performed admirably. The Convolutional neural network was able to achieve a 92% accuracy rate
RESEARCH GAP: To correctly identify and categories diverse dermatological disorders, the skin disease detection system described in this invention makes use of cutting-edge machine learning algorithms, including deep learning methods like CNNs. It has a user-friendly interface, sophisticated feature extraction algorithms, and a diverse and extensive dataset for training that can be seamlessly integrated with current healthcare systems. In comparison to preceding patents, the system places a strong emphasis on thorough testing and validation, ensuring improved accuracy, dependability, and accessibility. It represents a significant development in dermatology diagnostics, enhancing patient care and redefining the identification and management of skin diseases.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Photographic Dermatological Diagnosis Tool.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The system described in the current invention accurately identifies and categorizes a variety of skin illnesses using artificial intelligence (AI) and machine learning (ML) approaches. The system seeks to offer dermatologists, medical professionals, and people looking for early identification and diagnosis of skin diseases an effective and dependable option.
Traditional techniques of skin disease diagnosis frequently rely on dermatologists' subjective, time-consuming, and prone to error visual inspection. By utilizing the strength of AI and ML algorithms to examine digital photographs of the skin and offer automated, impartial evaluations, the proposed approach eliminates these limitations. A reliable and expandable infrastructure supports the skin disease detection system, which also includes picture acquisition tools like cameras, smartphones, and specialized dermoscopy equipment. Preprocessing improves the quality and removes noise from the captured images, providing the best input for further analysis. The sophisticated AI/ML algorithms at the center of the system are trained using enormous datasets of annotated skin photos. These databases cover a broad spectrum of skin disorders, such as melanoma, eczema, psoriasis, acne, and numerous fungal infections. Using feature extraction, dimensionality reduction, and classification algorithms throughout the training process, the system can pick up on complex patterns and traits linked to various skin illnesses.
The system examines the previously processed skin photos throughout the detection process and extracts pertinent parameters such lesion characteristics, color, texture, and shape. The trained ML models receive these features after which they employ classification algorithms to precisely detect and categories the skin condition. The technology offers real-time feedback and generates a thorough report with the disease diagnosed, confidence ratings, and suggested therapies or additional diagnostic steps. Extensive validation studies are carried out, comparing the system's performance to evaluations made by top dermatologists, to assure the system's dependability and correctness. The outcomes show the system's capacity for high sensitivity and specificity, confirming its efficacy as a useful diagnostic tool. The skin disease detection system has a great deal of promise to revolutionize the dermatological field since it provides a non-invasive, practical, and affordable alternative for early identification, prompt intervention, and better patient outcomes. This invention, which makes use of AI and ML, offers a novel method to deal with the difficulties in diagnosing skin diseases, opening the door to improved dermatology care delivery.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
FIGURE 2: FLOW DIAGRAM
FIGURE 3: RESULT SET
FIGURE 4: RESULT GRAPH
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Technical Description:
The skin disease identification device described herein combines imaging technology with artificial intelligence (AI) to enable quick and accurate diagnosis of various skin conditions. It consists of hardware components such as a camera or imaging sensor, a processor for image analysis, memory for storing data and algorithms, and a display unit to present diagnostic results to the user.
1. Hardware Components:
1.1 Camera or Imaging Sensor:
The device is equipped with a high-resolution camera or imaging sensor capable of capturing detailed photographs of skin areas. It may include features such as auto-focus, image stabilization, and adjustable aperture to ensure clear and accurate image capture.
1.2 Processor:
A powerful processor is integrated into the device to handle image processing tasks and execute the AI algorithms. It processes captured images in real-time, enhancing clarity, adjusting color balance, and optimizing image quality for accurate analysis. The processor may include multi-core CPUs or specialized AI processors to efficiently perform complex computations required for disease identification.
1.3 Memory:
The device includes onboard memory to store a database of skin disease images, associated metadata, and AI models. It may utilize non-volatile memory such as flash storage for storing large datasets and quick access during image analysis. The memory also stores user settings, diagnostic history, and software updates.
1.4 Display Unit:
A high-resolution display unit is incorporated to present diagnostic results to the user. It may be a touchscreen display for intuitive interaction, capable of displaying detailed information about identified skin diseases. The display will be capable of rendering images and text with high clarity and color accuracy.
Upon powering on the device using the on/off button (101), the user positions the device over the skin area of concern. Pressing the click button (104) activates the camera (102) to capture a digital image of the skin. The captured image is processed internally by the device's processor to enhance quality and clarity. Simultaneously, the AI algorithm analyzes the processed image to identify potential skin diseases based on visual characteristics such as color variations, lesion patterns, and texture abnormalities. Once analysis is complete, the diagnostic results are displayed on the result screen (106). If necessary, the buzzer (103) may emit alerts or notifications to indicate completion of image capture or to signal the availability of diagnostic results. The display screen (105) provides real-time feedback and prompts to guide the user through each step of the process, ensuring ease of use and understanding. The device is designed for both personal and professional use, offering a convenient and accessible tool for preliminary skin disease diagnosis. Its integration of advanced imaging technology with AI-driven analysis enhances accuracy and efficiency in identifying various skin conditions, thereby facilitating early detection and appropriate medical intervention.
2. Software and Algorithms:
The device may suggest differential diagnoses based on similarity metrics and clinical guidelines. Image capture, preprocessing, feature extraction, classification, and diagnosis are all steps in the multi-step process that goes into the AI/ML system that automatically detects skin diseases. The system's primary technological elements are as follows:
Preprocessing: The obtained images go through preprocessing procedures to improve their quality and get rid of any noise or artefacts that can interfere with the analysis that comes after. Resizing, normalization, noise reduction, and picture enhancement are some methods used in preprocessing.
Feature Extraction: From the extracted images, pertinent features are retrieved to capture key traits suggestive of various skin conditions. Texture, color, shape, and structural patterns are a few examples of these characteristics. Common feature extraction techniques include the Haar wavelet transform, the histogram of oriented gradients (HOG), and convolutional neural networks (CNN) based on deep neural networks.
Classification: For categorization, the retrieved features are passed into neural networks or machine learning algorithms. The system can be trained using labelled datasets using a variety of supervised learning algorithms, including support vector machines (SVM), random forests, or models based on deep learning like CNNs, recurrent neural networks (RNNs), or hybrid architectures. Learning the patterns and connections between the characteristics and relevant skin conditions constitutes the instructional phase.
Diagnose: After the classification model has been trained, it can be used to forecast the existence of skin conditions in test photos that have not yet been observed. The algorithm calculates a probability or confidence score for each probable ailment by comparing the test image's retrieved features to the patterns it has learned. The approach creates a diagnosis or a prioritized list of possible skin illnesses based on these ratings, assisting dermatologists in making decisions.
3. Disease Classification and Identification:
Post image processing, the AI algorithm compares the extracted features with its knowledge base to classify the skin condition.
It provides a probability score or confidence level for each identified disease, along with relevant diagnostic information.
Non-Technical Description: Photographic Dermatological Diagnosis Tool utilizing AI/ML is a cutting-edge tool created to help doctors diagnose skin disorders precisely. This technology uses artificial intelligence and machine learning to analyses photos of the skin and detect a variety of diseases, from common problems like acne and eczema, to ones that might be fatal, like melanoma. Digital cameras, smartphones, or specialized devices can be used to take dermatological photos for use with this system. The photos are then enhanced and any undesired features are removed through processing. As important markers for various skin illnesses, the algorithm then pulls pertinent aspects from the photos, such as texture, color, shape, and structural patterns.
Artificial intelligence algorithms or deep learning models use these extracted attributes to categories the photos into several disease groups. In the learning phase, the algorithm builds correlations between the features and the related skin diseases using a large database of labelled photos. Once instructed, the system can examine fresh, unveiled photos, and identify skin conditions. The system assigns likelihood or confidence levels for each potential disease by comparing the features obtained of the test image with the learnt patterns.
The system for detecting skin diseases presented here uses machine learning algorithms to identify, categories, and treat a variety of skin problems. Images of skin lesions collected by various imaging tools, such as cameras or cellphones, are fed to the system. The system analyses the photos and offers precise diagnoses using a combination of pre-processing methods, feature extraction, and machine learning models, giving dermatologists a crucial tool for speedy and accurate skin disease identification.
To provide an accurate diagnosis, the skin disease detection system uses several connected modules. The modules include:
a) Training Data: A diversified dataset of labelled skin lesion photos is used as the training data for the creation of the skin disease identification system. These photos show a wide variety of dermatological problems, such as various skin conditions and various sizes, colors, and textures of lesions. The machine learning models are taught to recognize patterns and traits that set one skin condition apart from another using training data as the basis. In order to reduce the inaccuracy in identifying the proper disease class, the machine learning models' parameters are iteratively adjusted during the training process using the labelled image inputs. By identifying the statistical relationships and patterns present in the images, the models gain knowledge from the training data and can make precise predictions during the testing phase.
b) Testing Data: The system goes through testing to gauge its performance and generalization skills after the models are trained on the labelled dataset. A different collection of labelled skin lesion photos that were not used during training make up the testing data. The capacity of the algorithm to correctly detect skin illnesses based on unobserved data is evaluated using these photos that imitate real-world settings. The trained models process the testing data during testing, and their predictions are compared to the labels assigned to the known ground truth. Performance measures are calculated to assess how well the system performs in correctly diagnosing and categorizing various skin disorders, including accuracy, precision, recall, and F1-score. In the testing step, it is ensured that the system generalizes well and gives an estimate of its correctness and dependability in real world.
c) Image Acquisition: Skin lesion images are either taken with conventional imaging equipment or transferred from digital sources.
d) Pre-processing: To enhance the presentation of the input data, the captured images are processed using techniques for noise reduction, normalization, and image enhancement.
e) Feature Extraction: The method makes use of sophisticated computer vision algorithms to retrieve pertinent details from the pre-processed photos, concentrating on the traits of skin lesions.
f) Machine Learning Models: The approach trains robust models on a broad dataset of labelled skin lesion photos by combining supervised and unsupervised learning approaches. These models, which enable precise classification and diagnosis, include convolutional neural networks (CNNs), support vector machines (SVMs), and aggregation approaches.
g) Diagnosis and Classification: To determine the existence of dermatological disorders, the trained models analyze the retrieved features and compare them to the taught patterns. A thorough diagnostic report is produced by the system, detailing the detected ailment, its severity, and any available treatments.
h) User Interface: Dermatologists may interact with the skin diagnosis system, review diagnostic findings, and access extra features including historical patient data, recommendations for treatment, and educational resources thanks to its user-friendly interface.
In this device, we used machine learning algorithms to categories skin disease classes using ensemble approaches, and then used a feature selection method to compare the findings produced. Specialist can detect the disease type with the help of a web-based framework which is developed in Python Django frame- work. In the proposed device, we present a novel approach to detect the skin disease. Here we have used Support Vector Machine (SVM) Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) classifiers to identify the disease. Specialist need to upload the image and Deep learning algorithms will predict the disease and display the accuracy. The proposed model is easy to use, but it also provides a higher level of accuracy. As a result of this model, we were able to achieve a 95% accuracy rate in the diagnosis of various skin conditions. The proposed system provides a state of art accuracy for early skin disease detection
In Figure 4, graph shows how the model performed through 20 epochs, on both training and validation data, plus loss. A: The training accuracy (blue line) increases steadily, showing that the model is learning from its data. At the same time, an increase in validation accuracy (red line) but flattens to a point after 10 epochs which shows that the model never performed better beyond these amount of new data points. This is reflected by the decrease in training loss (green line), which indicates that our model predicts with greater confidence values close to ground truth on the overall dataset. On the other hand, we have validation loss (red line): which decreases initially; but then flattens and starts to rise again at around 75th epoch.... overfitting... right? The trained model performs great on the training data, only to perform relatively poorly in generalizing its recommendations when getting validation (unseen) data. In general, the model shows good early learning capability; however, a stable stage in validation accuracy and some increase in validation loss towards later epochs of training show that regularization techniques or an adaptation to training duration should be applied aptly for exploring more idealized generality performance against overfitting on both train/free datasets.
ADVANTAGES: -
Better Accuracy: The system makes use of cutting-edge AI algorithms and machine learning models, which can go through enormous amounts of data and spot intricate patterns. As a result, skin disease detection and diagnosis are more accurate and reliable. Reduced misdiagnosis risk is achieved through the system's ability to distinguish between illnesses with similar outward appearances and to deliver precise diagnoses.
Early detection and intervention are essential in the management of many skin conditions, especially those with the potential to have life-threatening implications like melanoma. Early detection of skin irregularities and diseases is made possible by the automatic skin disease detection system, allowing for prompt intervention and treatment. This may result in improved patient outcomes and raised possibilities for effective treatment.
Efficiency in terms of time and money: Traditional dermatological examinations, which involve specialists manually inspecting and analyzing skin lesions, frequently take a lot of time and money. Because it can process and analyses images quickly, the automated system considerably cuts down on the amount of time needed for diagnosis. This can increase productivity in healthcare settings and save doctors and patient’s important time. Additionally, it might lower the overall expense of many clinic visits, pointless biopsies, and other procedures.
Scalability and accessibility: The system is remotely accessible, enabling dermatological evaluation and diagnosis from any place. This is especially helpful where access to dermatologists with specialized training is limited. Because of the system's scalability, it can manage many cases at once, ensuring prompt and accurate diagnosis even during busy times. Additionally, it can be combined with telemedicine platforms, allowing patients to get advice from professionals without going to a dermatologist office.
Assistance for Dermatologists' Decisions: The computerized skin disease detection system is a useful resource for dermatologists who need to make decisions. They get more data and analysis from it to help in their diagnosis. The method helps dermatologists make educated judgements and raises diagnostic accuracy by providing a prioritized list of probable diseases with related probability.
Continuous Learning and Improvement: The system's AI/ML components have the capacity to continuously learn from their experiences and boost their performance over time. The system can evolve and improve its algorithms as more data is gathered and incorporated into it, which improves accuracy and diagnostic capabilities. The system is kept up to date with the most recent advancements in dermatology and is capable of efficiently managing new skin conditions thanks to this ongoing learning process.
, Claims:1. A method for Photographic Dermatological Diagnosis Tool using AI/ML, comprising steps of:
Acquiring digital images of the skin;
Preprocessing the acquired images to enhance their quality and remove noise;
Extracting relevant features from the pre-processed images;
Utilizing machine learning algorithms or deep learning models to classify the images into specific skin disease categories;
Generating a diagnosis or a ranked list of potential skin diseases based on the classification results;
wherein the acquired images are obtained from digital cameras, smartphones, or specialized imaging devices.
2. The method as claimed in claim 1, wherein the preprocessing steps include resizing, normalization, noise reduction, and image enhancement techniques.
3. The method as claimed in claim 1, wherein the feature extraction includes extracting texture, color, shape, and structural patterns from the pre-processed images.
4. The method as claimed in claim 1, wherein the machine learning algorithms comprise support vector machines (SVM), random forests, or deep learning models such as convolutional neural networks (CNN) or recurrent neural networks (RNN).
5. The method as claimed in claim 1, wherein the trained machine learning models compare the extracted features of test images with learned patterns to assign probabilities or confidence scores for each potential skin disease.
6. A computer-readable medium comprising instructions for implementing the method of claim 1.
7. The system as claimed in claim 1, further comprising a user interface for displaying the diagnosis or ranked list of potential skin diseases to dermatologists.
8. The system as claimed in claim 1, wherein the machine learning means comprise a processor executing machine learning algorithms or deep learning models.