Novel Hybrid Deep Learning Model for Enhancing Skin Cancer Detection

Abstract

Skin Cancer is the most serious form of cancer, often spreading to other parts of the body over time. Early and accurate identification of skin lesions can improve the treatment options and increase the probability of curing the disease before it progresses. However, automatic detection of skin cancer faces difficulties because of the imbalanced datasets, affected areas covered by hair or moles, and other visual obstructions. In recent years, Deep Learning (DL) techniques have been widely used to address these challenges. Despite advancements, achieving consistently high accuracy remains difficult. To tackle these challenges, the research proposes a novel hybrid DL model for efficient skin cancer detection from dermoscopic images. The proposed network consists of three key modules: feature extraction using the Xception network, feature enhancement through the Squeeze and Excitation network (SENet), and final classification using a transformer module. This combination of DL models effectively identifies SC. The proposed network is evaluated using benign and malignant dermoscopic images collected from the Kaggle website. Necessary preprocessing steps, such as resizing, grayscale conversion, hair removal, and data balancing, are applied. The proposed network is compared with popular DL networks such as MobileNet, Xception, and VGG-16. The experimental outcome demonstrates that the proposed network outperforms these models, achieving a higher accuracy, recall, precision, and F1 score of 96.83%, 97%, 96.67%, and 96.83%, respectively. The metrics value shows that the proposed network is promising and can be helpful for dermatologists in identifying skin cancer with minimal effort.