College of Engineering and IT, University of Dubai, United Arab Emirates.
Department of Electronics and Communication Engineering, TKM College of Engineering, Kollam, 691 005, India.
Comput Biol Med. 2022 Nov;150:106170. doi: 10.1016/j.compbiomed.2022.106170. Epub 2022 Oct 4.
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.
皮肤癌是一种恶性疾病，每年影响着全球数百万人。它是一种侵袭性疾病，其特征是体内皮肤细胞异常增殖，通过淋巴结繁殖和扩散，杀死周围组织。由于生活方式的改变和追求阳光的行为，皮肤癌的病例数正在上升。由于皮肤癌是一种致命的疾病，早期诊断和分级对于挽救生命至关重要。在这项工作中，应用了最先进的人工智能方法来开发一种独特的深度学习模型，该模型集成了 Xception 和 ResNet50。通过结合两个强大网络的特性，该网络实现了最高的准确性。所提出的串联 Xception-ResNet50（X-R50）模型可以将皮肤肿瘤分类为基底细胞癌、黑色素瘤、黑色素细胞痣、皮肤纤维瘤、光化性角化病和上皮内癌、血管和非癌良性角化病样病变。所提出的方法的性能与 DeepCNN 和其他最先进的迁移学习模型进行了比较。数据集 HAM10000 评估了所提出的方法的性能。对于这项研究，使用了 10500 张皮肤图像。该模型使用滑动窗口技术进行训练和测试。所提出的串联 X-R50 模型是最先进的，预测准确率为 97.8%。该模型的性能还通过使用方差分析（ANOVA）的统计假设检验进行了验证。所报道的方法既准确又高效，可以帮助皮肤科医生和临床医生在临床过程的早期阶段发现皮肤癌。
