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Volume 14
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10.3390/app14125233
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Article

Integrating Principal Component Analysis and Multi-Input Convolutional Neural Networks for Advanced Skin Lesion Cancer Classification

by
Rakhmonova Madinakhon
1,
Doniyorjon Mukhtorov
1 and
Young-Im Cho
2,*
1
Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Republic of Korea
2
Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(12), 5233; https://doi.org/10.3390/app14125233
Submission received: 11 May 2024 / Revised: 12 June 2024 / Accepted: 12 June 2024 / Published: 17 June 2024
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Abstract

The importance of early detection in the management of skin lesions, such as skin cancer, cannot be overstated due to its critical role in enhancing treatment outcomes. This study presents an innovative multi-input model that fuses image and tabular data to improve the accuracy of diagnoses. The model incorporates a dual-input architecture, combining a ResNet-152 for image processing with a multilayer perceptron (MLP) for tabular data analysis. To optimize the handling of tabular data, Principal Component Analysis (PCA) is employed to reduce dimensionality, facilitating more focused and efficient model training. The model’s effectiveness is confirmed through rigorous testing, yielding impressive metrics with an F1 score of 98.91%, a recall of 99.19%, and a precision of 98.76%. These results underscore the potential of combining multiple data inputs to provide a nuanced analysis that outperforms single-modality approaches in skin lesion diagnostics.
Keywords: skin cancer detection; multi-input deep learning; Principal Component Analysis (PCA); medical image augmentation skin cancer detection; multi-input deep learning; Principal Component Analysis (PCA); medical image augmentation

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MDPI and ACS Style

Madinakhon, R.; Mukhtorov, D.; Cho, Y.-I. Integrating Principal Component Analysis and Multi-Input Convolutional Neural Networks for Advanced Skin Lesion Cancer Classification. Appl. Sci. 2024, 14, 5233. https://doi.org/10.3390/app14125233

AMA Style

Madinakhon R, Mukhtorov D, Cho Y-I. Integrating Principal Component Analysis and Multi-Input Convolutional Neural Networks for Advanced Skin Lesion Cancer Classification. Applied Sciences. 2024; 14(12):5233. https://doi.org/10.3390/app14125233

Chicago/Turabian Style

Madinakhon, Rakhmonova, Doniyorjon Mukhtorov, and Young-Im Cho. 2024. "Integrating Principal Component Analysis and Multi-Input Convolutional Neural Networks for Advanced Skin Lesion Cancer Classification" Applied Sciences 14, no. 12: 5233. https://doi.org/10.3390/app14125233

APA Style

Madinakhon, R., Mukhtorov, D., & Cho, Y.-I. (2024). Integrating Principal Component Analysis and Multi-Input Convolutional Neural Networks for Advanced Skin Lesion Cancer Classification. Applied Sciences, 14(12), 5233. https://doi.org/10.3390/app14125233

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