Concatenated Xception-ResNet50 — A novel hybrid approach for accurate skin cancer prediction. (November 2022) - Panthakkan, Alavikunhu; Anzar, S.M.; Jamal, Sangeetha; Mansoor, Wathiq
Abstract: Skin cancer is a malignant disease that affects millions of people around the world every year. It is an invasive disease characterised by an abnormal proliferation of skin cells in the body that multiply and spread through the lymph nodes, killing the surrounding tissue. The number of skin cancer cases is on the rise due to lifestyle changes and sun-seeking behaviour. As skin cancer is a deadly disease, early diagnosis and grading are crucial to save lives. In this work, state-of-the-art AI approaches are applied to develop a unique deep learning model that integrates Xception and ResNet50. This network achieves maximum accuracy by combining the properties of two robust networks. The proposed concatenated Xception-ResNet50 (X-R50) model can classify skin tumours as basal cell carcinoma, melanoma, melanocytic nevi, dermatofibroma, actinic keratoses and intraepithelial carcinoma, vascular and non-cancerous benign keratosis-like lesions. The performance of the proposed method is compared with a DeepCNN and other state-of-the-art transfer learning models. The Human Against Machine (HAM10000) dataset assesses the suggested method's performance. For this study, 10, 500 skin images were used. The model is trained and tested with the sliding window technique. The proposed concatenated X-R50 model is cutting-edge, with a 97.8% prediction accuracy. The performance of the model is also validated by a statistical hypothesis test using analysis of variance (ANOVA). The reported approach is both accurate and efficient and can help dermatologists and clinicians detect skin cancer at an early stage of the clinical process. Graphical abstract: Highlights: A novel Concatenated Xception-ResNet50 model is proposed for skin cancer prediction. The model is trained and tested with the sliding window technique. The proposed model achieves an unprecedented classification accuracy of 97.8%. Statistical tests using ANOVA demonstrates the superior performance of the model. The proposed technique could be used to accelerate skin cancer screening.