Description:To evaluate the detection model, we use quadratic weighted kappa (or Cohen’s Kappa), which measures the consensus between expert ratings and submitted ratings. The quadratic weights trigger an optimization factor in the rounding operation. This metric ranges from 0 (random agreement among raters) to 1 (complete agreement among raters). A perfect score of 1.0 is allowed when both the actuals and predictions are same; otherwise the least possible score is -1 which is provided when the predictions are furthest away from actuals. In this work, we treat all actuals as 0’s and all predictions as 4’s. This will give rise a quadratic weighted kappa score of -14. we are going to optimize mean squared error (MSE) and by optimizing MSE, we will also optimize quadratic weighted kappa as the problem is expressed as regression. This section reports the evaluation result which is the predicted labels of DR for blind spot. For evaluation we predict values from the generator and round it to the nearest integer to get valid predictions. After that we compute the quadratic. We perform Grid search in order to optimize the validation score over a range of thresholds and the quadratic weighted kappa score is 0.92 with threshold range of (0.5, 1.5, 2.5, 3.5). Our fine-tuned EfficientNet-B5 based model scores 92.32% on validation set with the input image size of 224x224.We perform test time augmentation (same augmentation steps as mentioned in above section) 10 times and averaged all 5 models with their TTA predictions. We adopted several convolutional neural network based models reported in Table 2 and found that DenseNet169 perform significantly better than ResNet 50 which is since former model improves on Adam optimizer. The prediction scores of convolutional neural network models reported in the identification problem as regression which we also considered for our proposed model on top of EfficientNet-B5 model. The models are trained in general without any fine-tuning steps and EfficientNet-B5 performs better than any other CNN models with a significant improvement of 0.10%. IoT based healthcare systems are more powerful by integration of deep learning approaches. Recent advancements in IoT technology have revolutionized the electronic healthcare research and industry applications. The huge increase in the use of portable smart health devices has increased the quality of health monitoring, diagnosis and collecting data for the clinicians with potential to perform early diagnosis and provide necessary treatment on time. However, the use of personal medical data and records is a concern of data security and data sharing policy. Blockchain provides a solution to deal with privacy and transparency of data. The typical IoT Blockchain platform for smart healthcare. The framework consists of vital sign monitoring system, an IoT server, a blockchain network, and a communication interface to collect the patients information from the healthcare sensors. All the information is stored securely and communicated to the medical staff for further diagnosis and treatment. This information further can be used to develop decision making models using deep learning models to provide accurate diagnosis in time. The approach once development can be optimized to develop an IoT based smart device and can be used by the medical staff efficiently. Introduce the state-of-the-art DL based smart health system for identification of blindness in eye disease (diabetic retinopathy) evaluated on a retinal image dataset in the IoT. We have shown that the convergence of IoT with AI can lead to provide effective smart health system. Our fine-tuned Efficient-B5 based model outperforms CNN and ResNet50 models with 92.32% validation accuracy which predicts diagnosis of diabetic retinopathy severity (eye blindness) on the five-point scale from retinal images. Our baseline model Efficient-B5 (with fine-tuning) model training on the average doctor opinion, a tactics that output state-of-the-art results on identifying blindness by 90.20% of validation accuracy. The freezing and unfreezing for fine- tuned EfficientNet-B5 significantly improved the prediction with 92.32% validation accuracy. The proposed approach has been developed and tested only for the early detection of diabetic retinopathy in diabetic patients. We also performed oversampling strategies for the interpretation on our detected results. For other medical image diagnosis, the approach needs to be tested before making any concluding remarks. We found that there are more number of 0’s and 2’s i.e., no diabetic retinopathy symptom and moderate retinopathy shown in around 89% of the images.Intend to adopt other CNN architectures such as UNet with ResNet models and EfficientNet weights with UNet for such imbalanced image collection, and pseudo labeling the imbalanced dataset may potentially improve the prediction for given classes including consideration of this identification task as binary classification by individually labeling the data could be an added advantage. The limitation in doing so is of processing power which greatly increases if using EfficientNet-B6 or B7 weights. The proposed approach has been developed and tested only for the early detection of diabetic retinopathy in diabetic patients. , Claims:1) An optimized technique on top of recently released pre-trained EfficientNet models for blindness identification in retinal images along with a comparative analysis among various other neural network models.
2) Our fine-tuned EfficientNet-B5 based model evaluation follows the benchmark dataset of retina images captured using fundus photography during varied imaging stages and outperforms CNN and ResNet50 models.