A Scalable and Generalised Deep Learning Framework for Anomaly Detection in Surveillance Videos DOI Creative Commons
Sabah Abdulazeez Jebur, Laith Alzubaidi, Ahmed Saihood

et al.

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, vandalism. While deep learning (DL) has shown excellent performance this area, existing approaches have struggled apply DL models across different anomaly tasks without extensive retraining. This repeated retraining time‐consuming, computationally intensive, unfair. To address limitation, a new framework introduced study, consisting three key components: transfer enhance feature generalization, model fusion improve representation, multitask classification generalize classifier multiple training from scratch when task introduced. The framework’s main advantage its ability requiring for each task. Empirical evaluations demonstrate effectiveness, achieving an accuracy 97.99% on RLVS (violence detection), 83.59% UCF dataset (shoplifting 88.37% both datasets using single Additionally, tested unseen dataset, achieved 87.25% 79.39% violence shoplifting datasets, respectively. study also utilises two explainability tools identify potential biases, ensuring robustness fairness. research represents first successful resolution generalization issue detection, marking significant advancement field.

Language: Английский

Towards unbiased skin cancer classification using deep feature fusion DOI Creative Commons

Ali Atshan Abdulredah,

Mohammed A. Fadhel, Laith Alzubaidi

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 31, 2025

Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing width augmentation to enhance efficiency. The proposed model addresses potential biases associated with conditions, particularly in individuals darker tones or excessive hair, by incorporating fusion assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, Melanoma Skin Cancer-comprising images categorized into benign classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, employed interpretability model’s decisions. Comparative analysis was performed three pre-existing learning networks-EfficientNet, MobileNet, Darknet. results demonstrate superiority, achieving an accuracy 99.86% F1 score 99.95%, underscoring its efficacy gradient propagation capture across various levels. research highlights significant advancing classification, providing robust tool accurate early diagnosis. integration enhances mitigates hair tones. outcomes this contribute improved patient healthcare practices, showcasing exceptional capabilities classification.

Language: Английский

Citations

1
A Scalable and Generalised Deep Learning Framework for Anomaly Detection in Surveillance Videos DOI Creative Commons
Sabah Abdulazeez Jebur, Laith Alzubaidi, Ahmed Saihood

et al.

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, vandalism. While deep learning (DL) has shown excellent performance this area, existing approaches have struggled apply DL models across different anomaly tasks without extensive retraining. This repeated retraining time‐consuming, computationally intensive, unfair. To address limitation, a new framework introduced study, consisting three key components: transfer enhance feature generalization, model fusion improve representation, multitask classification generalize classifier multiple training from scratch when task introduced. The framework’s main advantage its ability requiring for each task. Empirical evaluations demonstrate effectiveness, achieving an accuracy 97.99% on RLVS (violence detection), 83.59% UCF dataset (shoplifting 88.37% both datasets using single Additionally, tested unseen dataset, achieved 87.25% 79.39% violence shoplifting datasets, respectively. study also utilises two explainability tools identify potential biases, ensuring robustness fairness. research represents first successful resolution generalization issue detection, marking significant advancement field.

Language: Английский

Citations

0