Version 1
: Received: 30 December 2022 / Approved: 3 January 2023 / Online: 3 January 2023 (08:45:52 CET)
How to cite:
Lyakhov, P.; Lyakhova, U.; Kalita, D. Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function. Preprints2023, 2023010022. https://doi.org/10.20944/preprints202301.0022.v1
Lyakhov, P.; Lyakhova, U.; Kalita, D. Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function. Preprints 2023, 2023010022. https://doi.org/10.20944/preprints202301.0022.v1
Lyakhov, P.; Lyakhova, U.; Kalita, D. Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function. Preprints2023, 2023010022. https://doi.org/10.20944/preprints202301.0022.v1
APA Style
Lyakhov, P., Lyakhova, U., & Kalita, D. (2023). Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function. Preprints. https://doi.org/10.20944/preprints202301.0022.v1
Chicago/Turabian Style
Lyakhov, P., Ulyana Lyakhova and Diana Kalita. 2023 "Multimodal Neural Network System for Skin Cancer Recognition with a Modified Cross-Entropy Loss Function" Preprints. https://doi.org/10.20944/preprints202301.0022.v1
Abstract
Currently, skin cancer is the most commonly diagnosed form of cancer in humans and is one of the leading causes of death in patients with cancer. Biopsy methods are an invasive research method and are not always available for primary diagnosis. Imaging methods have low accuracy and depend on the experience of the dermatologist. Artificial intelligence technologies can match and surpass visual analysis methods in accuracy, but they have the risk of a false negative response when a malignant pigmented lesion can be recognized as benign. One possible way to improve accuracy and reduce the risk of false negatives is to analyze heterogeneous data, combine different preprocessing methods, and use modified loss functions to eliminate the negative impact of unbalanced dermatological data. The paper proposes a multimodal neural network system with a modified cross-entropy loss function that is sensitive to unbalanced heterogeneous dermatological data. The accuracy of recognition in 10 diagnostically significant categories for the proposed system was 85.19%. The novelty of the proposed system lies in the use of cross-entropy loss when training the modified function with the help of weight coefficients. The introduction of weighting factors has reduced the number of false negative forecasts, as well as improved accuracy by 1.02-4.03 percentage points compared to the original multimodal systems. The introduction of the proposed multimodal system as an auxiliary diagnostic tool can reduce the consumption of financial and labor resources involved in the medical industry, as well as increase the chance of early detection of skin cancer.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
