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Updated: Aug 23, 2025

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

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Published on: August 30, 2013

Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method.

Theyazn H H Aldhyani¹, Rajit Nair², Elham Alzain¹

¹Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|October 27, 2022

Summary

This summary is machine-generated.

This study introduces a novel deep learning model for early breast cancer detection using histological images. The enhanced model accurately identifies small cancer cells, improving diagnostic capabilities and saving lives.

Keywords:

AlexNet VGG16 network accurate detection convolution neural network data augmentation deep learning dilation convolution

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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Published on: April 21, 2023

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Breast cancer is a global health concern, with early detection crucial for survival.
Histopathology is vital for breast cancer diagnosis, but challenges like color variation exist.
Deep learning models show promise in improving breast cancer detection accuracy.

Purpose of the Study:

To develop an advanced deep learning model for accurate detection of small, early-stage breast cancer cells in histological images.
To address challenges in histological image analysis, such as color divergence and feature recognition.
To enhance medical research and technological development in breast cancer diagnostics.

Main Methods:

Utilized the BreCaHAD dataset for histological annotation and diagnosis.
Employed data augmentation with 19 factors (scale, rotation, gamma) to prevent overfitting.
Developed a hybrid dilation deep learning model incorporating dilation convolution and max pooling for multi-scale feature extraction.
Integrated a dilated unit with Alexnet using a dilated residual expanding kernel model for recognizing minute objects and thin borders.

Main Results:

The proposed hybrid dilation deep learning model demonstrated improved recognition of key features, edges, curves, and colors.
The model effectively processed multi-scale information through dilation convolution and max pooling.
Achieved an Area Under the Curve (AUC) of 96.15%, indicating superior performance compared to existing methods.

Conclusions:

The novel hybrid dilation deep learning model significantly enhances the accuracy of breast cancer cell detection from histological images.
The model's ability to recognize minute objects and thin borders offers a substantial advancement in early cancer diagnosis.
This research contributes to the field of AI in medicine, paving the way for more effective breast cancer screening tools.