J 2022

Visualizing CoAtNet Predictions for Aiding Melanoma Detection

KVAK, Daniel

Základní údaje

Originální název

Visualizing CoAtNet Predictions for Aiding Melanoma Detection

Název anglicky

Visualizing CoAtNet Predictions for Aiding Melanoma Detection

Autoři

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

Odkazy

DOI

Klíčová slova anglicky

skin cancer; melanoma; computer-aided diagnostics; image classification; CoAtNet; convolutional neural networks; deep learning

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 5. 7. 2022 17:10, Mgr. Daniel Kvak

Anotace

ORIG EN

V originále

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.

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.