Visualizing CoAtNet Predictions for Aiding Melanoma Detection

KVAK, Daniel. Visualizing CoAtNet Predictions for Aiding Melanoma Detection. 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.

Základní údaje

• Originální název: Visualizing CoAtNet Predictions for Aiding Melanoma Detection
• Název anglicky: Visualizing CoAtNet Predictions for Aiding Melanoma Detection
• Autoři: KVAK, Daniel.
• Vydání: Engineering and Technology Journal, Mandsaur University, India, Everant Journals, 2022, 2456-3358.

Další údaje

• Typ výsledku: Článek v odborném periodiku
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Doi: http://dx.doi.org/10.47191/etj/v7i7.01
• Klíčová slova anglicky: skin cancer; melanoma; computer-aided diagnostics; image classification; CoAtNet; convolutional neural networks; deep learning
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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.

Anotace anglicky

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.