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Correction: Abushark et al. Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data. <i>Sensors</i> 2025, <i>25</i>, 731.

Sensors (Basel, Switzerland)·2026

Same author

Retraction notice to "Enhancing security in smart healthcare systems: Using intelligent edge computing with a novel Salp Swarm Optimization and radial basis neural network algorithm" [Heliyon 10 (2024) e33792].

Heliyon·2025

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Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach.

Brain and behavior·2025

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Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data.

Sensors (Basel, Switzerland)·2025

Same author

Enhancing security in smart healthcare systems: Using intelligent edge computing with a novel Salp Swarm Optimization and radial basis neural network algorithm.

Heliyon·2024

Same author

A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique.

Diagnostics (Basel, Switzerland)·2023

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Updated: Jul 25, 2025

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

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

Published on: August 16, 2020

Anomaly Detection of Breast Cancer Using Deep Learning.

Ahad Alloqmani¹, Yoosef B Abushark¹, Asif Irshad Khan¹

¹Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Arabian Journal for Science and Engineering

|June 26, 2023

Summary

This study introduces a deep learning framework for accurate breast cancer anomaly detection. The model effectively identifies abnormalities, improving early diagnosis and patient outcomes.

Keywords:

Anomaly detection Breast cancer Deep learning Diagnosis Mammography

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Last Updated: Jul 25, 2025

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

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Published on: August 16, 2020

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Published on: December 15, 2014

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

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

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer remains a leading cause of death for women globally.
Early detection and treatment are crucial for improving patient survival rates and reducing healthcare costs.
Imbalanced datasets are a common challenge in medical AI research.

Purpose of the Study:

To propose an efficient and accurate deep learning framework for detecting breast abnormalities (benign and malignant).
To address the challenge of imbalanced data in medical image analysis.
To develop a system capable of recognizing anomalies by learning from normal data.

Main Methods:

A two-stage framework involving image pre-processing and feature extraction.
Utilizing a pre-trained MobileNetV2 model for efficient feature extraction.
Employing a single-layer perceptron for the final classification step.

Main Results:

The framework demonstrated high efficiency and accuracy in anomaly detection, with Area Under the Curve (AUC) ranging from 81.40% to 97.36%.
The proposed method outperformed existing relevant works in detecting breast abnormalities.
The framework successfully addressed limitations of previous approaches in handling imbalanced medical data.

Conclusions:

The developed deep learning framework offers a promising solution for accurate and efficient breast cancer anomaly detection.
The approach effectively handles imbalanced datasets, a significant hurdle in medical AI.
This work contributes to advancing early breast cancer diagnosis through artificial intelligence.