Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine Tuning Approach

Shelatkar, Tejas; Bansal, Urvashi

doi:10.1007/978-981-99-0047-3_10

Cited by 5 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This design integrates both an AM and deep supervision into U-Net, which facilitates both high-and low-resolution representation of features, a boon especially for smaller tumor areas. Meanwhile, Shelatkar et al [23] unveiled a cross-stage local network structure. This structure is geared towards better feature extraction in U-Net.…”

Section: Brain Tumor Image Segmentationmentioning

confidence: 99%

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

Rahman,

Zhang,

Bhutto

2023

Symmetry

View full text Add to dashboard Cite

The symmetrical segmentation of brain tumor images is crucial for both clinical diagnosis and computer-aided prognosis. Traditional manual methods are not only asymmetrical in terms of efficiency but also prone to errors and lengthy processing. A significant barrier to the process is the complex interplay between the deep learning network for MRI brain tumor imaging and the harmonious compound of both local and global feature information, which can throw off the balance in segmentation accuracy. Addressing this asymmetry becomes essential for precise diagnosis. In answer to this challenge, we introduce a balanced, end-to-end solution for brain tumor segmentation, incorporating modifications that mirror the U-Net architecture, ensuring a harmonious flow of information. Beginning with symmetric enhancement of the visual quality of MRI brain images, we then apply a symmetrical residual structure. By replacing the convolutional modules in both the encoder and decoder sections with deep residual modules, we establish a balance that counters the vanishing gradient problem commonly faced when the network depth increases. Following this, a symmetrical threefold attention block is integrated. This addition ensures a balanced fusion of local and global image features, fine-tuning the network to symmetrically discern and learn essential image characteristics. This harmonious integration remarkably amplifies the network’s precision in segmenting MRI brain tumors. We further validate the equilibrium achieved by our proposed model using three brain tumor segmentation datasets and four metrics and by juxtaposing our model against 21 traditional and learning-based counterparts. The results confirm that our balanced approach significantly elevates performance in the segmentation of MRI brain tumor images without an asymmetrical increase in computational time.

show abstract

Section: Brain Tumor Image Segmentationmentioning

confidence: 99%

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

Rahman,

Zhang,

Bhutto

2023

Symmetry

View full text Add to dashboard Cite

show abstract

YOLOv3 for Brain Tumor Detection

Chefrour,

Drissi,

Kechaichia

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning

Almufareh,

Imran,

Khan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Recent advancements in image processing and computer vision have brought significant transformations in healthcare technology, leading to significant improvements in diagnosis accuracy, costeffectiveness, and time efficiency. Magnetic Resonance Imaging (MRI) is employed by the radiologist for its remarkable ability to detect even the most subtle brain abnormalities. This study considers a comprehensive analysis of the two prominent object identification frameworks, YOLOv5 and YOLOv7, leveraging state-ofthe-art deep learning architectures to classify and detect brain cancers within MRI. The brain tumor dataset encompasses three distinct classes, including meningiomas, gliomas and pituitary tumors. To ensure precise segmentation of the tumor regions, the preprocessing phase incorporates advanced mask alignment techniques. This preprocessed dataset has been used to evaluate the performance of the deep learning models for brain tumor detection and classification. From the numerical results of YOLOv5, it was noticed that a recall score of 0.905 for box detection and 0.906 for mask segmentation, with a precision score of 0.94 and 0.936, respectively. At an IoU threshold of 0.5, both box detection and mask segmentation achieve a mAP of 0.947, whereas, at an IoU threshold of 0.5 to 0.95, they achieve mAPs of 0.666 and 0.657, respectively. In comparison, YOLOv7 exhibits strong performance with box detection accuracy of 0.936 and a mask segmentation accuracy of 0.935. The recall score are 0.904 for box detection and mask segmentation is 0.903. Notably, the mAP result at the IoU threshold of 0.5 are 0.94 for box detection and mask segmentation is 0.941. Over the broader IoU spectrum of 0.5 to 0.95, the mAP was 0.677 for box detection and 0.659 for mask segmentation. To underscore the novelty of the approach, the performance of the proposed framework is systematically compared with established methods such as RCNN, Faster RCNN, and Mask RCNN.INDEX TERMS brain tumor, deep learning, image processing, MRI, YOLO

show abstract

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine Tuning Approach

Cited by 5 publications

References 18 publications

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

A Symmetrical Approach to Brain Tumor Segmentation in MRI Using Deep Learning and Threefold Attention Mechanism

YOLOv3 for Brain Tumor Detection

Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning

Contact Info

Product

Resources

About