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Related Experiment Video

Updated: Sep 20, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on

K Shankar¹, Ashit Kumar Dutta², Sachin Kumar¹

¹Big Data and Machine Learning Laboratory, South Ural State University, 454080 Chelyabinsk, Russia.

|June 10, 2022

Summary

This summary is machine-generated.

A new deep learning model, CSSADTL-BCC, accurately classifies breast cancer from histopathological images. This automated approach improves upon manual diagnosis, offering a precise and efficient tool for pathologists.

Keywords:

breast cancer cancer computer aided diagnosis deep learning histopathological images medical imaging

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Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

Area of Science:

Medical Imaging
Computational Pathology
Artificial Intelligence in Oncology

Background:

Histopathological diagnosis is the gold standard for breast cancer detection but is time-consuming and subjective.
Automated analysis of histopathological images is crucial to overcome diagnostic challenges and workload.
Deep learning has shown significant promise in advancing breast cancer pathological image classification.

Purpose of the Study:

To introduce a novel deep learning model for accurate breast cancer classification using histopathological images.
To develop an automated system that overcomes the limitations of manual histopathological analysis.
To enhance the precision and efficiency of breast cancer diagnosis through advanced computational methods.

Main Methods:

A novel chaotic sparrow search algorithm with deep transfer learning-enabled breast cancer classification (CSSADTL-BCC) model was developed.
Gaussian filtering (GF) was used for noise reduction, and MixNet for feature extraction.
A stacked gated recurrent unit (SGRU) model classified images, with hyperparameters optimized by the chaotic sparrow search algorithm (CSSA).

Main Results:

The CSSADTL-BCC model demonstrated superior performance in breast cancer classification on a benchmark dataset.
The model achieved high accuracy, outperforming existing state-of-the-art approaches.
Hyperparameter optimization of the SGRU model using CSSA proved effective for histopathological image analysis.

Conclusions:

The CSSADTL-BCC model offers a precise and automated solution for breast cancer classification from histopathological images.
This novel approach, utilizing hyperparameter-tuned SGRU, represents a significant advancement in computational pathology.
The study highlights the potential of deep learning and optimization algorithms to improve cancer diagnosis accuracy and efficiency.