Description:Field and Background of the Invention
Derma Scan AI: Smart Lesion Segmentation and Early Melanoma Detection with Deep Learning focuses on the integration of artificial intelligence into dermatological diagnostics, specifically in the automation of skin lesion segmentation and melanoma detection from dermoscopic and clinical images. This work lies at the intersection of computer vision, deep learning, and digital dermatology, with the objective of providing accurate, rapid, and accessible diagnostic support for the early detection of skin cancer. The approach leverages convolutional neural networks, transformer-based architectures, and advanced image segmentation algorithms to identify lesion boundaries precisely and classify malignancy risk levels.
This technology is particularly relevant in teledermatology applications, enabling integration into mobile platforms, handheld dermatoscopes, and hospital imaging systems for point-of-care diagnosis. It serves both as a decision-support tool for dermatologists and as a potential screening method for primary care providers in settings where access to specialists is limited. Skin cancer remains the most common form of cancer globally, with melanoma representing its most aggressive and lethal variant. According to the World Health Organization, between 132,000 and 150,000 new melanoma cases are diagnosed annually worldwide, and the incidence continues to rise due to increased ultraviolet exposure and an aging population. While early-stage melanoma is highly treatable with survival rates exceeding 90%, prognosis declines dramatically once the cancer progresses to advanced stages.

Traditional diagnostic workflows rely on visual inspection and dermoscopy, which require substantial expertise and experience. Even for trained dermatologists, distinguishing between benign and malignant lesions can be challenging, particularly in early stages when visual differences are subtle. The scarcity of specialist dermatological services in many regions results in delayed diagnosis and poorer patient outcomes. Recent advances in digital imaging and deep learning have enabled the development of automated systems capable of matching or exceeding human-level accuracy in medical image interpretation. In dermatology, AI-driven lesion analysis involves two essential steps: segmentation, which accurately delineates the lesion from surrounding skin to define the region of interest, and classification, which determines whether the lesion is malignant or benign based on learned visual features.
Conventional machine learning methods required handcrafted features such as color distribution, texture metrics, and symmetry measures, but these approaches often failed to capture the complexity of early melanoma. Deep learning models, particularly convolutional neural networks and attention-based architectures, can automatically extract highly discriminative features directly from large annotated datasets, improving robustness and reducing reliance on manual feature engineering.
Summary of the Invention
Skin cancer continues to be a major public health challenge, with melanoma recognized as one of the most aggressive and life-threatening types. Early diagnosis remains the most effective way to increase survival rates, yet conventional diagnostic approaches depend largely on visual assessment and clinical experience, which can lead to subjective interpretations and occasional misdiagnosis.

To address this challenge, Derma Scan AI is introduced as an intelligent deep learning framework designed to support dermatologists in the early detection of melanoma. The system employs convolutional neural networks capable of performing precise lesion segmentation, ensuring that even small and irregular boundaries of suspicious regions are identified accurately. Beyond segmentation, the model integrates a classification mechanism that distinguishes between benign and malignant cases with high reliability, reducing the chances of false positives and false negatives. The combined use of segmentation and classification within a single pipeline represents the key innovation, enabling the system to extract meaningful visual features while enhancing diagnostic precision. Extensive testing on publicly available dermoscopic datasets demonstrates strong performance in terms of accuracy, sensitivity, and specificity, outperforming conventional machine learning–based solutions. By providing a reliable decision-support tool, this research aims to reduce the burden on dermatologists, facilitate faster and more consistent melanoma screening, and ultimately contribute to timely interventions and improved patient care. With its ability to merge automation and clinical relevance, Derma Scan AI highlights the transformative role of artificial intelligence in modern healthcare and sets a foundation for broader applications in dermatological diagnostics.
Brief Description of the System
Melanoma is a highly aggressive form of skin cancer that poses a serious threat to global health, making early and accurate detection essential for saving lives. Traditional diagnostic practices rely mainly on dermatologist expertise and visual examination, which, while valuable, are often subject to variability, human error, and time constraints. To overcome these limitations, Derma Scan AI is designed as an intelligent deep learning framework that automates the process of skin lesion analysis.

The system uses convolutional neural networks to perform detailed lesion segmentation, allowing it to capture irregular boundaries and subtle variations in dermoscopic images that might otherwise go unnoticed. Once segmented, the lesions are classified into benign or malignant categories, creating an integrated pipeline that enhances diagnostic precision while reducing false detections. The novelty of the approach lies in combining segmentation and classification within a single model, enabling both accurate boundary mapping and reliable disease prediction. Experiments conducted on publicly available dermoscopic datasets demonstrate that the framework achieves high levels of accuracy, sensitivity, and specificity, surpassing the performance of traditional machine learning techniques. By acting as a decision-support tool, Derma Scan AI aims to assist dermatologists in early melanoma screening, offering faster and more consistent results while reducing diagnostic workload. Ultimately, this innovation contributes to timely medical intervention, improved treatment outcomes, and better patient care, showcasing the significant potential of artificial intelligence in advancing healthcare diagnostics and supporting clinical practice.
After conducting fundamental pre-processing procedures, data augmentation was performed on the generated data. Data augmentation is an image processing technique that creates modified copies of the original images. Developing diverse variations of authentic images expands the dataset and enhances the generalization capabilities of deep learning models. Figure 1 illustrates the block diagram of the proposed MCADT model. For classifying skin lesions, specifically melanoma, the “Human Against Machine Melanoma” (HAM10000) dataset, listed in Table 1, is frequently used in dermatology and machine learning. 10 015 Dermoscopic images of skin lesions, including benign and malignant melanocytic lesions, are included in the HAM10000 dataset.

Hospitals in many nations were among the sources of these images. Relevant metadata, such as patient characteristics, lesion location, and diagnosis, are linked to each image. We have the HAM1K dataset in the form of Melanoma or not Melanoma. From here, we have grouped 7 classes into different batches to train our classifier. The Aki and Vasc classes given in Table 1 are stated as Melanoma or no Melanoma and are hence grouped into batches for training and then classifying Melanoma or Not Melanoma based on the training. The Aki and Vasc could be harmful depending on the severity of the skin in the images which could lead to the classification as Melanoma or Not Melanoma. We have divided the 10 015 images dataset into training, validation, and testing by performing preprocessing steps.
In Figure 1, block 1 illustrates the image processing procedures depicted in Figure 2 that are essential for model input. Upon completion of image preprocessing, the images are placed in the image database. In block 2, we implemented data augmentation techniques, resulting in an enhanced augmented dataset stored in the augmented database. In block 3, we have integrated the Original HAM10K dataset with the enhanced dataset to train the proposed MCADT model. Upon completion of the model training, we implemented evaluation methodologies and conducted testing of the model. The model ultimately predicts whether the provided sample is melanoma or not. 6

TABLE1. HAM 10000 Dataset. Class Training images Validation images Testing images Total images Prediction
Aki 236 26 65 327 Melanoma or no melanoma
Bc 371 41 102 514 Melanoma
Bk 792 88 219 1099 No melanoma
Df 72 8 35 115 No melanoma
Mela 802 89 222 1113 Melanoma
Nv 4828 536 1341 6705 No melanoma
Vasc 103 11 28 142 Melanoma or No melanoma

Objectives
1. To develop an automated system for early melanoma detection using deep learning.
2. To design a robust lesion segmentation model that accurately identifies skin lesion boundaries.
3. To extract and analyze discriminative image features from dermoscopic data for reliable classification.
4. To integrate segmentation and classification for enhanced diagnostic precision.
5. To evaluate the system’s performance on standard dermoscopy datasets in terms of accuracy, sensitivity, and specificity.
6. To compare the proposed framework with conventional machine learning methods.

Newness
The primary objective of this research is to build an intelligent system that supports dermatologists in the early detection of melanoma, thereby improving treatment outcomes and patient care. Unlike conventional diagnostic methods that rely heavily on manual expertise, the proposed framework introduces an automated approach that combines lesion segmentation with classification in a single deep learning pipeline. This integration enhances boundary detection accuracy while simultaneously improving diagnostic reliability. By leveraging convolutional neural networks to capture subtle visual patterns, the system reduces false detections and achieves higher sensitivity compared to traditional methods. The novelty of this work lies in unifying segmentation and detection to deliver a clinically useful decision-support tool that not only assists specialists but also accelerates the diagnostic process. Through evaluation on publicly available dermoscopy datasets, the system demonstrates strong performance, highlighting its potential to transform melanoma screening into a faster, more consistent, and more effective process.

, Claims:We claim:
1. Melanoma is one of the deadliest forms of skin cancer, and early identification is critical for effective treatment.
2. Existing diagnostic methods rely heavily on dermatologist expertise, which may introduce subjectivity and variability.
3. We propose *Derma Scan AI*, a deep learning–based system for automatic skin lesion segmentation and melanoma detection.
4. The framework employs convolutional neural networks to accurately capture lesion boundaries and extract discriminative image features.
5. Integration of segmentation with classification improves diagnostic precision and lowers false detection rates.
6. Evaluation on publicly available dermoscopy datasets shows strong accuracy, sensitivity, and specificity.
7. Comparative results demonstrate that our approach performs better than traditional machine learning methods.