Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach
ABSTRACT Melanoma has a higher tendency to spread to other parts of the human body swiftly if not detected and treated timely. This makes melanoma more dangerous than any other skin cancer disease. Melanoma is a type of skin cancer that develops from the melanocytes. The melanocyte is a genuine skin...
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Wiley
2025-03-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70096 |
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| author | Justice Williams Asare Emmanuel Akwah Kyei Seth Alornyo Emmanuel Freeman Martin Mabeifam Ujakpa William Leslie Brown‐Acquaye Alfred Coleman Forgor Lempogo |
| author_facet | Justice Williams Asare Emmanuel Akwah Kyei Seth Alornyo Emmanuel Freeman Martin Mabeifam Ujakpa William Leslie Brown‐Acquaye Alfred Coleman Forgor Lempogo |
| author_sort | Justice Williams Asare |
| collection | DOAJ |
| description | ABSTRACT Melanoma has a higher tendency to spread to other parts of the human body swiftly if not detected and treated timely. This makes melanoma more dangerous than any other skin cancer disease. Melanoma is a type of skin cancer that develops from the melanocytes. The melanocyte is a genuine skin cell that protects the skin pigment known as melanin. Melanoma has recently become a significant and growing public health concern globally. It is marked by the incidence of millions of new cases annually, encompassing both non‐melanoma and melanoma skin cancer. This disease is characterized by the unchecked proliferation of abnormal skin cells, with the potential to metastasize to other anatomical sites. Conventional diagnostic approaches, particularly biopsy‐based methods, are invasive, time‐consuming, and often culminate in treatment delays and increased patient discomfort. This study assessed their effectiveness in detecting melanoma by applying three distinct deep learning techniques, specifically EfficientNetB3, MobileNetV2, and InceptionV3. Among these architectures, EfficientNetB3 emerged as the standout performer, achieving an exceptional accuracy rate of 90.7% and an impressive area under the curve (AUC) score of 97%. The cascading combination technique was then utilized to develop a multi‐architecture model. With the cascading multi‐architecture technique, we combined all the layers (multiple layers) output of the models and processed them (the output of the multiple layers) in a structured pipeline, which improves upon the previous output. The results of the multi‐architecture model, with an accuracy of 94.86%, signify the optimal architecture for melanoma detection. |
| format | Article |
| id | doaj-art-0c238bc410cb4e8596023dae37429dc6 |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-0c238bc410cb4e8596023dae37429dc62025-08-20T03:44:06ZengWileyEngineering Reports2577-81962025-03-0173n/an/a10.1002/eng2.70096Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning ApproachJustice Williams Asare0Emmanuel Akwah Kyei1Seth Alornyo2Emmanuel Freeman3Martin Mabeifam Ujakpa4William Leslie Brown‐Acquaye5Alfred Coleman6Forgor Lempogo7Faculty of Applied Science and Technology Koforidua Technical University Koforidua GhanaFaculty of Computing and Information Systems Ghana Communication Technology University Accra GhanaFaculty of Applied Science and Technology Koforidua Technical University Koforidua GhanaFaculty of Computing and Information Systems Ghana Communication Technology University Accra GhanaSchool of Governance, IT and Management University of KwaZulu Natal Durban South AfricaFaculty of Computing and Information Systems Ghana Communication Technology University Accra GhanaFaculty of Computing and Information Systems Ghana Communication Technology University Accra GhanaFaculty of Computing and Information Systems Ghana Communication Technology University Accra GhanaABSTRACT Melanoma has a higher tendency to spread to other parts of the human body swiftly if not detected and treated timely. This makes melanoma more dangerous than any other skin cancer disease. Melanoma is a type of skin cancer that develops from the melanocytes. The melanocyte is a genuine skin cell that protects the skin pigment known as melanin. Melanoma has recently become a significant and growing public health concern globally. It is marked by the incidence of millions of new cases annually, encompassing both non‐melanoma and melanoma skin cancer. This disease is characterized by the unchecked proliferation of abnormal skin cells, with the potential to metastasize to other anatomical sites. Conventional diagnostic approaches, particularly biopsy‐based methods, are invasive, time‐consuming, and often culminate in treatment delays and increased patient discomfort. This study assessed their effectiveness in detecting melanoma by applying three distinct deep learning techniques, specifically EfficientNetB3, MobileNetV2, and InceptionV3. Among these architectures, EfficientNetB3 emerged as the standout performer, achieving an exceptional accuracy rate of 90.7% and an impressive area under the curve (AUC) score of 97%. The cascading combination technique was then utilized to develop a multi‐architecture model. With the cascading multi‐architecture technique, we combined all the layers (multiple layers) output of the models and processed them (the output of the multiple layers) in a structured pipeline, which improves upon the previous output. The results of the multi‐architecture model, with an accuracy of 94.86%, signify the optimal architecture for melanoma detection.https://doi.org/10.1002/eng2.70096deep learningdetectionmelanomamulti‐architecture |
| spellingShingle | Justice Williams Asare Emmanuel Akwah Kyei Seth Alornyo Emmanuel Freeman Martin Mabeifam Ujakpa William Leslie Brown‐Acquaye Alfred Coleman Forgor Lempogo Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach Engineering Reports deep learning detection melanoma multi‐architecture |
| title | Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach |
| title_full | Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach |
| title_fullStr | Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach |
| title_full_unstemmed | Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach |
| title_short | Application of Medical Images for Melanoma Detection Using a Multi‐Architecture Convolutional Neural Network From a Deep Learning Approach |
| title_sort | application of medical images for melanoma detection using a multi architecture convolutional neural network from a deep learning approach |
| topic | deep learning detection melanoma multi‐architecture |
| url | https://doi.org/10.1002/eng2.70096 |
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