AI-Powered Skin Cancer Detection with Dual-Phase Deep Learning, Clinical Fusion and Explainable AI
Year: 2026
Type: article
Abstract: Skin cancer is among the most common cancers worldwide, and melanoma found late has high mortality, yet dermatologists are scarce in rural and low-resource regions. This project, “AI-Powered Skin Cancer Detection with Dual-Phase Deep Learning, Clinical Fusion and Explainable AI”, builds a decision-support system that works from smartphone photographs and a short clinical questionnaire instead of dermoscopy equipment. Phase 1 is an EfficientNet-B3 classifier trained on ISIC 2019 (8 lesion classes, lesion-level split). Phase 2 is a two-head fusion model that joins the image embedding with 14 clinical features and is fine-tuned on the smartphone dataset PAD-UFES-20 (6 classes, patient-level split). A rule-based red-flag layer covers six rare cancers that have no public training images. Grad-CAM, ABCD dermoscopy scoring and SHAP explain each prediction. Phase 1 reached 0.647 ± 0.022 balanced accuracy and 0.579 ± 0.020 macro F1 in 5-fold cross-validation. Phase 2 reached 0.689 macro F1 and 0.704 balanced accuracy with clinical fusion, against 0.694 and 0.736 for the image-only head, so fusion did not improve accuracy. A fairness audit shows a 0.247 sensitivity gap between light and medium skin tones on DDI, and no malignant case detected in the DermaCon-IN neoplasm subset (52 images, Fitzpatrick III-V only, no light-skin baseline). The system is intended to support triage and referral, not to diagnose.
Institution: Sandip Institute of Technology & Research Centre
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7223891192