Design and Implementation of a Custom Lightweight CNN with Integrated XAI for Diagnostic Skin Lesion Analysis

ABSTRACT The differentiation of benign and malignant skin lesion types is an essential problem in medical images, which facilitates diagnosis and treatment of skin cancer. Traditional deep learning approaches usually depend on the use of the pre-trained models, which are not customized to the specific dataset or task. In this work, to tackle the challenge, a Custom Residual-based Depth-wise Separable Lightweight Inception Model with Custom Attention Mechanism is proposed together with Explainable AI (XAI) techniques. It is written from scratch, using only bottleneck convolutions (1x1) and spatially separable convolutions (1x3,3x1), which makes it really lightweight and efficient. The approach includes developing a new model architecture based on residuals and multi-scale (which known as Inception-style) feature extraction using a novel attention mechanism that guides the network on the important image features. Furthermore, XAI methods such as Grad-CAM, LIME, and SHAP are employed to analyze and explain the model's output, facilitating transparency and reliability in the medical domain. This coursework train and test the model on an accessible skin lesion dataset, using extensive data preprocessing, data augmentation, and class balancing. The results show that the proposed model is superior, with highest accuracy, precision, recall, and F1-score as well as good ROC-AUC and PR-AUC. The XAI visualizations also further confirm the model’s attention on clinically relevant regions of the images, which makes the model more interpretable. In conclusion, a deep learning model that is lightweight, interpretable, and efficient for skin lesion classification is proposed. The combination of PI layers and XAI methodologies guarantees a good level of performance and transparency, thus proving to be an effective instrument for the analysis of medical images.