A Deep Learning Approach for Brain Tumor Firmness Detection Using YOLOv4

Alhussainan, Norah Fahd; Youssef, Belgacem Ben; Ismail, Mohamed Maher Ben

doi:10.1109/tsp55681.2022.9851237

Cited by 3 publications

(1 citation statement)

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“…Comparative analysis against state-of-the-art models highlights their superiority. YOLOv7, utilizing the SGD optimizer, a batch size of 64, and a learning rate of 0.01, achieves outstanding overall performance [8]. YOLO models outperform the commonly used two-stage detection algorithm, Faster R-CNN, in fracture detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Real-Time Object Detection- A Comparison of YOLO Models

Sharma,

Tyagi,

Dubey

2024

IJIRCST

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Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in weapon detection. We train YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on a custom dataset of 16,000 images containing guns, knives, and heavy weapons. Each model is evaluated on a validation set of 1,400 images, with mAP (mean average precision) as the primary performance metric. This extensive comparative analysis identifies the best performing YOLO variant for gun and weapon detection, providing valuable insights into the strengths and weaknesses of each model for this specific task.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Real-Time Object Detection- A Comparison of YOLO Models

Sharma,

Tyagi,

Dubey

2024

IJIRCST

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A Deep Learning Approach to Detect Breast Cancer Disease from Mammogram Images

Sohel,

Hossain,

Hasan

et al. 2023

2023 26th International Conference on Computer and Information Technology (ICCIT)

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A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3–YOLOv7

Alhussainan,

Ben Youssef,

Ben Ismail

2024

Computation

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Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance images (MRIs), a process that is prone to human error and is also time consuming. Recent advancements leverage machine learning models to categorize tumors, such as distinguishing between “malignant” and “benign” classes. This study focuses on the supervised machine learning task of classifying “firm” and “soft” meningiomas, critical for determining optimal brain tumor treatment. The research aims to enhance meningioma firmness detection using state-of-the-art deep learning architectures. The study employs a YOLO architecture adapted for meningioma classification (Firm vs. Soft). This YOLO-based model serves as a machine learning component within a proposed CAD system. To improve model generalization and combat overfitting, transfer learning and data augmentation techniques are explored. Intra-model analysis is conducted for each of the five YOLO versions, optimizing parameters such as the optimizer, batch size, and learning rate based on sensitivity and training time. YOLOv3, YOLOv4, and YOLOv7 demonstrate exceptional sensitivity, reaching 100%. Comparative analysis against state-of-the-art models highlights their superiority. YOLOv7, utilizing the SGD optimizer, a batch size of 64, and a learning rate of 0.01, achieves outstanding overall performance with metrics including mean average precision (99.96%), precision (98.50%), specificity (97.95%), balanced accuracy (98.97%), and F1-score (99.24%). This research showcases the effectiveness of YOLO architectures in meningioma firmness detection, with YOLOv7 emerging as the optimal model. The study’s findings underscore the significance of model selection and parameter optimization for achieving high sensitivity and robust overall performance in brain tumor classification.

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A Deep Learning Approach for Brain Tumor Firmness Detection Using YOLOv4

Cited by 3 publications

References 0 publications

Optimizing Real-Time Object Detection- A Comparison of YOLO Models

Optimizing Real-Time Object Detection- A Comparison of YOLO Models

A Deep Learning Approach to Detect Breast Cancer Disease from Mammogram Images

A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3–YOLOv7

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