Sanga Prabhav, Singh Jaskaran, Dubey Arun Kumar, Khanna Narendra N, Laird John R, Faa Gavino, Singh Inder M, Tsoulfas Georgios, Kalra Mannudeep K, Teji Jagjit S, Al-Maini Mustafa, Rathore Vijay, Agarwal Vikas, Ahluwalia Puneet, Fouda Mostafa M, Saba Luca, Suri Jasjit S
Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India.
Global Biomedical Technologies, Inc., Roseville, CA 95661, USA.
Diagnostics (Basel). 2023 Oct 9;13(19):3159. doi: 10.3390/diagnostics13193159.
Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models' performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions.
皮肤病变分类在皮肤病学中起着至关重要的作用，有助于早期发现、诊断和处理危及生命的恶性病变。然而，单独的迁移学习（TL）模型未能提供最佳性能。在本研究中，我们提出了一种基于注意力机制的集成深度学习技术，这是一种强大、新颖且通用的方法，用于提取皮肤病变分类的特征。该技术通过使用七个预训练的TL模型进行分类，在提高诊断准确性方面具有巨大潜力。使用堆叠、softmax投票和加权平均技术创建了六个基于集成的深度学习（EBDL）模型。此外，我们将注意力机制作为一种有效范式进行研究，并在构建三个基于注意力机制的集成深度学习（aeEBDL）模型之前，创建了七个基于注意力机制的迁移学习（aeTL）模型，以创建一个可靠、自适应且通用的范式。TL模型的平均准确率为95.30%，使用基于集成的范式将其提高了4.22%，达到99.52%。aeTL模型的性能在准确率上比TL模型高出3.01%，aeEBDL模型比aeTL模型高出1.29%。统计测试显示，aeEBDL模型具有显著的p值和kappa系数以及99.6%的可靠性指数。该方法对于皮肤病变分类非常有效且具有通用性。
