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Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

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Fusing global context with multiscale context for enhanced breast cancer classification.

Niful Islam¹, Khan Md Hasib², M F Mridha³

¹Department of Computer Science and Engineering, United International University, Dhaka, 1212, Bangladesh.

Scientific Reports

|November 9, 2024

Summary

A novel fusion model combining Vision Transformer (ViT) and Atrous Spatial Pyramid Pooling (ASPP) achieves 100% accuracy for breast cancer classification from histopathological images, improving early detection.

Keywords:

Breast Cancer CNN Classification Transfer learning Vision Transformer

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Area of Science:

Oncology
Computer Vision
Medical Imaging

Background:

Breast cancer is a leading cause of cancer death in women.
Accurate classification of breast cancer from histopathological images is crucial for effective treatment.
Current Convolutional Neural Networks (CNNs) have limitations in capturing global and multi-scale features, impacting classification accuracy.

Purpose of the Study:

To develop an advanced fusion model for accurate breast cancer classification.
To overcome the limitations of CNNs by integrating global and multi-scale feature extraction.
To enhance the diagnostic performance for breast cancer detection using histopathological images.

Main Methods:

A fusion model integrating Vision Transformer (ViT) for global features and Atrous Spatial Pyramid Pooling (ASPP) for multi-scale features was developed.
An attention mechanism was incorporated into the model architecture.
A five-stage image preprocessing technique was applied to histopathological images.

Main Results:

The proposed fusion model achieved 100% accuracy on the BreakHis dataset at 100X and 400X magnification.
Classification accuracies of 99.25% at 40X and 98.26% at 200X magnification were recorded.
The model demonstrated robust performance across different magnification levels.

Conclusions:

The fusion model effectively classifies breast cancer from histopathological images.
The integration of ViT and ASPP enhances feature extraction capabilities for improved accuracy.
This model presents a dependable tool for proficient breast cancer classification and early detection.