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Updated: Oct 22, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN).

Maleika Heenaye-Mamode Khan¹, Nazmeen Boodoo-Jahangeer¹, Wasiimah Dullull¹

¹Department of Software and Information Systems, University of Mauritius, Reduit, Mauritius.

|August 26, 2021

Summary

Early breast cancer detection is crucial for survival. This study introduces an advanced deep learning model for improved classification of breast abnormalities from mammograms, achieving 88% accuracy.

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Early breast cancer detection significantly improves survival rates.
Radiologists face challenges in accurately identifying abnormalities in mammograms, leading to diagnostic errors.
Advancements in deep learning offer potential for enhanced breast cancer detection systems.

Purpose of the Study:

To develop a deep learning model for segmenting and classifying diverse breast abnormalities.
To improve upon existing methods by classifying specific abnormalities (calcifications, masses, asymmetry, carcinomas) rather than just benign/malignant.
To enhance the classification performance by adaptively adjusting the learning rate during neural network training.

Main Methods:

Utilized a Deep Convolutional Neural Network (CNN) architecture.
Implemented transfer learning using the pre-trained ResNet50 model on the breast imaging dataset.
Developed an enhanced deep learning model with an adaptive learning rate adjusted based on error curve changes.

Main Results:

The proposed deep learning model achieved an 88% performance rate in classifying four types of breast cancer abnormalities.
The model demonstrated effectiveness in segmenting and classifying specific abnormalities like masses, calcifications, carcinomas, and asymmetry.
The adaptive learning rate strategy contributed to improved classification accuracy.

Conclusions:

The developed deep learning model shows promise for more accurate and detailed breast cancer abnormality classification.
This approach can aid in improved disease management by providing more specific diagnostic information.
Further research in adaptive learning rate strategies can enhance the performance of AI in medical image analysis.