eprintid: 12752 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/27/52 datestamp: 2024-06-17 23:30:30 lastmod: 2024-06-17 23:30:31 status_changed: 2024-06-17 23:30:30 type: article metadata_visibility: show creators_name: Ahnaf Alavee, Kazi creators_name: Hasan, Mehedi creators_name: Hasnayen Zillanee, Abu creators_name: Mostakim, Moin creators_name: Uddin, Jia creators_name: Silva Alvarado, Eduardo René creators_name: de la Torre Diez, Isabel creators_name: Ashraf, Imran creators_name: Abdus Samad, Md creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: eduardo.silva@funiber.org creators_id: creators_id: creators_id: title: Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Diabetic retinopathy, transfer learning, CNN, Xception, inception, Grad-CAM abstract: Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for single-label (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We prese... date: 2024-05 publication: IEEE Access volume: 12 pagerange: 73950-73969 id_number: doi:10.1109/ACCESS.2024.3405570 refereed: TRUE issn: 2169-3536 official_url: http://doi.org/10.1109/ACCESS.2024.3405570 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for single-label (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We prese... metadata Ahnaf Alavee, Kazi; Hasan, Mehedi; Hasnayen Zillanee, Abu; Mostakim, Moin; Uddin, Jia; Silva Alvarado, Eduardo René; de la Torre Diez, Isabel; Ashraf, Imran y Abdus Samad, Md mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence. IEEE Access, 12. pp. 73950-73969. ISSN 2169-3536 document_url: http://repositorio.uneatlantico.es/id/eprint/12752/1/s41598-024-63831-0%20%281%29.pdf