DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images
Prabhav Sanga,
Jaskaran Singh,
Arun Kumar Dubey,
Narendra N. Khanna,
John R. Laird,
Gavino Faa,
Inder M. Singh,
Georgios Tsoulfas,
Mannudeep K. Kalra,
Jagjit S. Teji,
Mustafa Al-Maini,
Vijay Rathore,
Vikas Agarwal,
Puneet Ahluwalia,
Mostafa M. Fouda,
Luca Saba,
Jasjit S. Suri
Affiliations
Prabhav Sanga
Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India
Jaskaran Singh
Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
Arun Kumar Dubey
Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India
Narendra N. Khanna
Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi 110076, India
John R. Laird
Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
Gavino Faa
Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy
Inder M. Singh
Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
Georgios Tsoulfas
Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
Mannudeep K. Kalra
Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
Jagjit S. Teji
Department of Pediatrics, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
Mustafa Al-Maini
Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
Vijay Rathore
Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
Vikas Agarwal
Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
Puneet Ahluwalia
Department of Uro Oncology, Medanta the Medicity, Gurugram 122001, India
Mostafa M. Fouda
Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
Luca Saba
Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy
Jasjit S. Suri
Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
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.
