ENHANCED SKIN LESION CLASSIFICATION USING DEEP LEARNING MODEL IN INTERNET OF MEDICAL THINGS

The recent development of the Internet of Medical Things (IoMT) has greatly benefited medical professionals, patients and physicians in accessing medical information. This information helps to detect and recognize the diseases in the patients through medical images. However, the automatic detection and classification of diseases in medical images like skin lesions, brain tumor, breast cancer, etc., are still challenging in IoMT due to image quality and irrelevant features. To solve this issue, a Logistic Chaotic Map based Red Panda Optimization algorithm (LCM-RPO) is proposed for the feature selection process to select the significant features. Additionally, a Long Short–Term Memory (LSTM) model is utilised to classify the medical images effectively by learning the sequential information. Initially, the medical images are acquired and then raw images are preprocessed to enhance the data quality for further processing. The DenseNet-169 based feature extraction model extracts the most important features from the diseased portion. The experimental results of the proposed LCM-RPO-LSTM model attained an accuracy of 95.40% and 0.973 for Ph2 and ISIC-2016 datasets, which is higher than existing classification approaches such as PLDG and SVM-IARO.