An Interpretable Deep Learning Framework for Dermoscopic Lesion Classification for Reliable Skin Cancer Screening
An Interpretable Deep Learning Framework for Dermoscopic Lesion Classification for Reliable Skin Cancer Screening
Authors:
G chaithanya1, B ghanath Kumar Reddy2, Dr.M.Nisha3, Dr.P.divya4
Department of computer science and engineering Dr.M.G.R Educational and research institute,chennai,Tamilnadu
E-mail:chaithuyesh@gmail.com,ghanathkumarreddyboggadi@gmail.com,Nisha.cse@drmgrdu.ac.in,
Abstract— Skin cancer is considered one of the most common malignant conditions that will be diagnosed globally, and it needs efficient diagnostic aid to support the medical experts. Early detection is a key to a successful treatment and survival of patients. The standard methods of examination rely on the knowledge of a dermatologist, and this approach leads to inconsistency, a long time to assess, and inaccessibility where resources are limited. The research article proposes a deep learning-based diagnostic model that is implemented to be used on dermoscopic skin lesion imagery to classify the images as benign or malignant and interpretability tools to create visual heat maps that indicate the part of the image affected by the prediction result. The image preprocessing methods of normalization, resizing, and augmentation work to improve the quality of data and the generalization of the models. The training and evaluation is done on publicly available dermoscopic datasets with a variety of lesion categories. Experimental analysis has a high diagnostic potential, with a balance between sensitivity and specificity of about 96.3% in the classification of lesion types, which can be applied in practice with automated prediction of lesions, with visual interpretation maps to provide medical experts with insights into decision behavior and facilitate objective clinical evaluation. These predictions are interpretable, make diagnostic processes reliable, and can guide dermatologists. The general results show that there is a great possibility of smart dermatological screenings systems that can accelerate the early detection, reduce diagnostic variability, and assist in the efficient clinical decision-making process in contemporary healthcare settings.
Keywords— convolutional Neural Network (CNN), Visual Explanation Mapping, Gradient-weighted Class Activation Mapping (Grad-CAM), Malignant Lesion Identification, Medical Image Feature Extraction, Diagnostic Performance Assessment, Transparent Clinical Decision Support, ermoscopic Lesion Classification.