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Improved DeTraC Binary Coyote Net-Based Multiple Instance Learning for Predicting Lymph Node Metastasis of Breast Cancer From Whole-Slide Pathological Images.

The international journal of medical robotics + computer assisted surgery : MRCAS·2024

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An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification.

Ramkumar Muthukrishnan¹, Ashok Balasubramaniam², Vijaipriya Krishnasamy³

¹Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|February 8, 2025

Summary

Deep learning enhances breast cancer detection using a novel Lightweight Multihead attention Gannet Convolutional Neural Network (LMGCNN). This automated system achieves high accuracy in classifying mammograms, improving early diagnosis and patient outcomes.

Keywords:

attention mechanism breast cancer graphical user interface lightweight neural network mammograms

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Automated breast cancer detection in mammograms is crucial for early diagnosis.
Challenges include time constraints, feature extraction difficulties, and limited training data.
Deep learning offers a promising solution to these limitations.

Purpose of the Study:

To develop an automated deep learning system for improved breast cancer detection and classification in mammograms.
To address the challenges faced by medical professionals in mammogram analysis.

Main Methods:

Introduction of a Lightweight Multihead attention Gannet Convolutional Neural Network (LMGCNN) for mammogram classification.
Image enhancement techniques including wiener filtering, unsharp masking, and adaptive histogram equalization.
Feature extraction using Grey-Level Co-occurrence Matrix (GLCM) and optimized selection via a self-adaptive quantum equilibrium optimizer with artificial bee colony.

Main Results:

The LMGCNN model was evaluated on the CBIS-DDSM and MIAS datasets.
Achieved high accuracy rates of 98.2% and 99.9% on the respective datasets.
Demonstrated superior performance in breast cancer detection compared to existing models.

Conclusions:

The developed method shows significant potential for assisting in early and accurate breast cancer detection.
This advancement could lead to improved patient outcomes through timely diagnosis and treatment.