A deep learning framework for automated early diagnosis and classification of skin cancer lesions in dermoscopy images.

Skin cancer is a significant global health concern, and accurate and timely diagnosis is crucial for successful treatment. However, manual diagnosis can be challenging due to the subtle visual differences between benign and malignant lesions. This study introduces Skin-DeepNet, a novel deep learning-based framework designed for the automated early diagnosis and classification of skin cancer lesions from dermoscopy images. Skin-DeepNet incorporates a two-step pre-processing stage to enhance image contrast, followed by robust skin lesion segmentation using Mask R-CNN and GrabCut algorithm to achieve near-perfect segmentation accuracy (IOU up to 99.93%). Then a dual-feature extraction strategy is performed using a combination of a pre-trained high-resolution network (HRNet) model and attention block, which serve as feature descriptors. A deep belief networks (DBN) model is then trained on their outputs to capture high-level discriminative features. Finally, robust decision fusion strategies are employed to integrate the predictions of the proposed models using boosting and stacking to enhance overall Skin-DeepNet's accuracy. The Skin-DeepNet's performance has been validated on two challenging datasets: ISIC 2019 and HAM1000. The Skin-DeepNet system has outperformed the existing state-of-the-art systems by achieving an accuracy rate of 99.65% Precision of 99.51%, AUC of 99.94% on the ISIC 2019 dataset. Similarly, on the HAM1000 dataset, the Skin-DeepNet system demonstrated an accuracy rate, precision, and AUC of 100%, 99.92%, and 99.97%, respectively. These findings indicate that the developed Skin-DeepNet system can exhibit outstanding proficiency in accurately classifying skin lesions while aiding physicians in early diagnosis and treatment tasks in clinical settings.