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@article{1863517, author = {Kvak, Daniel}, article_location = {Mandsaur University, India}, article_number = {7}, doi = {http://dx.doi.org/10.47191/etj/v7i7.01}, keywords = {skin cancer; melanoma; computer-aided diagnostics; image classification; CoAtNet; convolutional neural networks; deep learning}, issn = {2456-3358}, journal = {Engineering and Technology Journal}, title = {Visualizing CoAtNet Predictions for Aiding Melanoma Detection}, url = {http://everant.org/index.php/etj/article/view/657/493}, year = {2022} }
TY - JOUR ID - 1863517 AU - Kvak, Daniel PY - 2022 TI - Visualizing CoAtNet Predictions for Aiding Melanoma Detection JF - Engineering and Technology Journal IS - 7 SP - 1322-1327 EP - 1322-1327 PB - Everant Journals SN - 24563358 KW - skin cancer KW - melanoma KW - computer-aided diagnostics KW - image classification KW - CoAtNet KW - convolutional neural networks KW - deep learning UR - http://everant.org/index.php/etj/article/view/657/493 N2 - Melanoma is considered to be the most aggressive form of skin cancer. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. At present, the evaluation of malignancy is performed primarily by invasive histological examination of the suspicious lesion. Developing an accurate classifier for early and efficient detection can minimize and monitor the harmful effects of skin cancer and increase patient survival rates. This paper proposes a multi-class classification task using the CoAtNet architecture, a hybrid model that combines the depthwise convolution matrix operation of traditional convolutional neural networks with the strengths of Transformer models and self-attention mechanics to achieve better generalization and capacity. The proposed multi-class classifier achieves an overall precision of 0.901, recall 0.895, and AP 0.923, indicating high performance compared to other state-of-the-art networks. ER -
KVAK, Daniel. Visualizing CoAtNet Predictions for Aiding Melanoma Detection. \textit{Engineering and Technology Journal}. Mandsaur University, India: Everant Journals, 2022, č.~7, s.~1322-1327. ISSN~2456-3358. Dostupné z: https://dx.doi.org/10.47191/etj/v7i7.01.
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