AN INTELLIGENT GUI-BASED SYSTEM FOR SKIN CANCER DETECTION AND CLASSIFICATION

Sheetalrani R. Kawale; Sanasultana Alagur; Akshata Sadalagi

doi:10.5281/zenodo.21175393

Abstract

Skin cancer is one of the most prevalent forms of cancer worldwide, and early detection is essential for improving patient survival rates and treatment outcomes. Traditional diagnosis techniques depend on eye inspection and dermoscopic analysis performed by dermatologists, which are often time-consuming and subject to human variability. To address these challenges, this study proposes an intelligent Convolutional Neural Network (CNN)- based framework for automated multiclass skin cancer detection and classification using the Skin Cancer MNIST (hmnist_28_28_RGB) dataset derived from the HAM10000 dataset. The dataset comprises 10,015 dermoscopic images belonging to seven skin lesion categories: Actinic Keratoses (AKIEC), Basal Cell Carcinoma (BCC), Benign Keratosis-like Lesions (BKL), Dermatofibroma (DF), Melanocytic Nevi (NV), Vascular Lesions (VASC), and Melanoma (MEL). Preprocessing methods for images, such as scaling, normalisation, and random To resolve class imbalance and enhance classification performance, oversampling and data augmentation were used. Convolutional layers, Batch Normalisation, Max-Pooling, Dropout regularisation, and fully linked layers make up the suggested CNN architecture, which efficiently extracts discriminative features from dermoscopic images. To attain effective convergence, the model was trained using the Adamax optimiser with the categorical crossentropy loss function. The suggested CNN model obtained an overall classification accuracy of 98.40%, according to experimental results, showing its effectiveness in accurately distinguishing among the seven skin lesion categories. Furthermore, a Flask-based Graphical User Interface (GUI) was developed to provide real-time skin lesion prediction with confidence scores through an intuitive web application. The proposed system serves as an efficient computer-aided diagnostic tool to assist healthcare professionals in the early identification and categorisation of skin cancer, facilitating prompt clinical decision-making and enhancing patient care.

Keywords

Deep Learning, Convolutional Neural Network (CNN), Skin Cancer MNIST (HAM10000), Dermoscopic Images, Image Classification, Random Oversampling, Data Augmentation, Flask Web Application.

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