Article: Establishment of artificial intelligence pathological feature diagnosis model and molecular mechanism Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2026 Vol.30 No.6 pp.1 - 20 Abstract: In the current diagnosis of cancer, the analysis of pathological section images and molecular markers (such as HER2, hormone receptor status, etc.) is usually performed separately, which can easily lead to difficulties in early identification, deviations in subtype classification, and limitations in personalized treatment decisions. This research solves this problem by establishing a breast cancer diagnosis model based on visual converter (ViT) and full connected neural network (FCNN). The experimental results show that the diagnostic model established in this study performs the best in terms of accuracy (0.963), recall rate (0.947), precision (0.952), and F1 score (0.950). In addition, the model shows high accuracy in classifying eight breast cancer subtypes in the cancer histopathological image dataset. The diagnostic model established in this study is helpful in promoting the development of precision medicine for cancer, improving the efficiency of clinical treatment, and has important practical value in reducing cancer mortality. Inderscience Publishers - linking academia, business and industry through research

Title: Establishment of artificial intelligence pathological feature diagnosis model and molecular mechanism

Authors: Yanping Zhang; Yuan Zhang; Haimiao Xu; Yuxi Wang

Addresses: Department of Pathology, Hangzhou Lin'an Traditional Chinese Medicine Hospital, China; Affiliated Hospital, Hangzhou City University, Hangzhou, 311300, Zhejiang, China ' Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 311300, Zhejiang, China ' Pathology Department, Zhejiang Cancer Hospital, Hangzhou, 311300, Zhejiang, China ' Department of Research and Education, Hangzhou Lin'an Traditional Chinese Medicine Hospital, China; Affiliated Hospital, Hangzhou City University, Hangzhou, 311300, Zhejiang, China

Abstract: In the current diagnosis of cancer, the analysis of pathological section images and molecular markers (such as HER2, hormone receptor status, etc.) is usually performed separately, which can easily lead to difficulties in early identification, deviations in subtype classification, and limitations in personalized treatment decisions. This research solves this problem by establishing a breast cancer diagnosis model based on visual converter (ViT) and full connected neural network (FCNN). The experimental results show that the diagnostic model established in this study performs the best in terms of accuracy (0.963), recall rate (0.947), precision (0.952), and F1 score (0.950). In addition, the model shows high accuracy in classifying eight breast cancer subtypes in the cancer histopathological image dataset. The diagnostic model established in this study is helpful in promoting the development of precision medicine for cancer, improving the efficiency of clinical treatment, and has important practical value in reducing cancer mortality.

Keywords: breast cancer diagnosis; vision transformer; ViT; fully connected neural network; FCNN; molecular markers; prognosis prediction.

DOI: 10.1504/IJDMB.2026.153894

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.6, pp.1 - 20

Received: 18 Oct 2025
Accepted: 15 Jan 2026
Published online: 29 May 2026 *

Title: Establishment of artificial intelligence pathological feature diagnosis model and molecular mechanism

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