A Modified MobileNetV3 Model Using an Attention Mechanism for Eight-Class Classification of Breast Cancer Pathological Images
Source abstract
Abstract
Addressing the challenge of achieving precise subtype classification of breast cancer histopathology images with limited resources, a lightweight model incorporating multi-stage information fusion and an attention mechanism is proposed for this task. Using MobileNetV3 as the backbone, a multi-stage fusion strategy captures the rich image information in breast cancer histopathology images. Additionally, the selective kernel (SK) attention mechanism is introduced in the initial stages of feature extraction, while an improved squeeze-and-excitation coordinate attention (SCA) mechanism is integrated in the later stages to enhance the extraction of both underlying and semantic features. The final feature representations for subtype classification are determined based on the attention map weights computed at each stage. The experimental results demonstrate the model’s outstanding recognition performance on the BreakHis dataset, achieving subtype classification accuracies of 96.259%, 94.763%, 95.511%, and 94.015% at four different magnifications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Radiomics and Machine Learning in Medical Imaging · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Science, Dalian Minzu University, Dalian 116600, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Liaoning Province of China · Liaoning Provincial Department of Education Basic Research Project for Higher Education Institutions—General Project
References (24)
Show source-provided references
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