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Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model.

Yahya Alqahtani¹, Umakant Mandawkar², Aditi Sharma³

¹Faculty of Computer Science and Information Technology, Jazan University, Jizan, Saudi Arabia.

Computational Intelligence and Neuroscience

|September 8, 2022

Summary

This study introduces a deep learning model for automated breast cancer classification from histopathology images, achieving 88.87% accuracy. The msSE-ResNet model enhances diagnostic speed and reduces errors in breast cancer pathology image analysis.

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Area of Science:

* Digital Pathology
* Medical Imaging Analysis
* Artificial Intelligence in Oncology

Background:

* Automated histopathological image identification is crucial for efficient and accurate cancer diagnosis.
* Computerized breast cancer multiclassification using histological images remains an under-explored area.
* Existing methods require improvement in accuracy and efficiency for clinical application.

Purpose of the Study:

* To develop a deep learning-based strategy for automated breast cancer pathology image classification.
* To introduce a multiscale channel recalibration model (msSE-ResNet) to enhance classification accuracy.
* To evaluate the performance of the proposed model on a public dataset for breast cancer classification.

Main Methods:

* Development of a channel refinement model incorporating an attention mechanism to reduce superfluous features.
* Implementation of a multiscale channel recalibration model to improve feature recalibration accuracy.
* Construction of the msSE-ResNet convolutional neural network, integrating multiscale properties and channel recalibration.

Main Results:

* The msSE-ResNet model achieved a classification accuracy of 88.87% for benign/malignant breast pathology images across various magnifications.
* The model demonstrated resilience with diseased images.
* Comparative analysis indicated that spatial recalibration models performed poorly on this specific task.

Conclusions:

* The msSE-ResNet deep learning model shows significant promise for automated breast cancer classification from histopathological images.
* The proposed multiscale channel recalibration approach effectively enhances classification accuracy.
* This automated system can potentially expedite diagnoses and reduce errors in breast cancer pathology.