Multibranch CNN With MLP-Mixer-Based Feature Exploration for High-Performance Disease Diagnosis

Zhou, Zixia; Islam, Md Tauhidul; Li, Xing

doi:10.1109/tnnls.2023.3250490

Cited by 11 publications

(1 citation statement)

References 34 publications

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“…The use of deep learning methods has become widespread in cancer detection systems, especially for large datasets. This area of research has been studied intensively in recent years [57][58][59]. Some of the leading methods are the MLP-Mixer method or the vision transformer model.…”

Section: Comparative Resultsmentioning

confidence: 99%

A Comparative Analysis of Skin Cancer Detection Applications Using Histogram-Based Local Descriptors

Aydin

2023

Diagnostics

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Among the most serious types of cancer is skin cancer. Despite the risk of death, when caught early, the rate of survival is greater than 95%. This inspires researchers to explore methods that allow for the early detection of skin cancer that could save millions of lives. The ability to detect the early signs of skin cancer has become more urgent in light of the rising number of illnesses, the high death rate, and costly healthcare treatments. Given the gravity of these issues, experts have created a number of existing approaches for detecting skin cancer. Identifying skin cancer and whether it is benign or malignant involves detecting features of the lesions such as size, form, symmetry, color, etc. The aim of this study is to determine the most successful skin cancer detection methods by comparing the outcomes and effectiveness of the various applications that categorize benign and malignant forms of skin cancer. Descriptors such as the Local Binary Pattern (LBP), the Local Directional Number Pattern (LDN), the Pyramid of Histogram of Oriented Gradients (PHOG), the Local Directional Pattern (LDiP), and Monogenic Binary Coding (MBC) are used to extract the necessary features. Support vector machines (SVM) and XGBoost are used in the classification process. In addition, this study uses colored histogram-based features to classify the various characteristics obtained from the color images. In the experimental results, the applications implemented with the proposed color histogram-based features were observed to be more successful. Under the proposed method (the colored LDN feature obtained using the YCbCr color space with the XGBoost classifier), a 90% accuracy rate was achieved on Dataset 1, which was obtained from the Kaggle website. For the HAM10000 data set, an accuracy rate of 96.50% was achieved under a similar proposed method (the colored MBC feature obtained using the HSV color space with the XGBoost classifier).

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Section: Comparative Resultsmentioning

confidence: 99%