2026-10-30T05:34:10Zhttps://munzur-rdlopen.rawdatalibrary.net/oai/request

oai:munzur-rdlopen.rawdatalibrary.net:munzur/22652026-10-28T15:29:38Zopenairecom_munzur_1col_munzur_2com_munzur_8col_munzur_154

Performance Analysis of Attention Mechanisms for Melanoma Cancer DetectionDogan, Gurkanattention mechanismcancer detectioncomputer visionconvolutional neural networkmelanoma cancerThe deadliest type of skin cancer worldwide is melanoma. If melanoma skin cancer is detected and treated at an early stage, the mortality rate can be significantly reduced. In particular, convolutional neural networks, one of the deep learning techniques that have achieved remarkable success in recent years in areas such as computer vision, natural language processing, signal processing, etc., can detect this type of cancer automatically, quickly and with high accuracy. Moreover, the accuracy performance of the neural network can be further increased by using attention mechanisms together with convolutional neural networks. However, attention mechanisms have not been used correctly and effectively in the neural network models proposed so far for the detection of melanoma skin cancer. This study examines the effect of attention mechanisms on the accuracy performance of a convolutional neural network and guides researchers to use these mechanisms correctly and effectively. To achieve this, a base model based on a convolutional neural network was built. Then, seven different attention mechanisms were integrated separately into this basic model and the results of the experimental tests of each were presented.Institute of Electrical and Electronics Engineers Inc.2024Conference Objecthttps://rdlopen.rawdatalibrary.net/r/munzur/2265https://doi.org/10.1109/idap64064.2024.10711100979-833153149-2en8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024info:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/conferenceObjecthttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/2265