A Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans

Bodapati, Jyostna Devi; Ahmed, Shaik Feroz; Chowdary, Yarra Yashwant; Sekhar, Konda Raja

doi:10.56578/ida020105

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In response to the identified need for computational efficiency within human security screening applications, attention was directed towards the development of lightweight CNN models. These models, epitomized by Yolov5s [17], GhostNet [18][19][20], MobileNet series [21][22][23], ShuffleNet series [24,25], EfficientNet series [26,27], and SqueezeNet series [28,29], were designed to balance recognition performance with reduced model complexity and computational demands.…”

Section: Lightweight Cnn Modelsmentioning

confidence: 99%

An Efficient Approach to Human Security Screening Image Recognition Through a Lightweight CNN Utilizing Yolov5s and GhostNet

Zhao,

Cheng

2023

View full text Add to dashboard Cite

Human security screening constitutes a vital component in public safety assurance across varied environments like airports, governmental edifices, and additional public spaces. Among the paramount challenges inherent in human security screening lies the immediate and precise discernment of prospective threats within X-ray images. Despite the potential exhibited by convolutional neural networks (CNNs) in image recognition tasks, including the detection of targets in X-ray imagery, the substantial computational burden and memory prerequisites often render real-time deployment impracticable on devices with limited resources. In the present study, a novel lightweight CNN approach, melding Yolov5s and GhostNet models with the coordinate attention mechanism, is introduced to alleviate the constraints found in existing techniques. By employing this combination, efficiency in computation and model accuracy has been augmented, thereby addressing the challenges of swift and accurate threat identification. Performance evaluation, conducted on a publicly accessible dataset comprising X-ray images pertinent to human security screening, demonstrated the superior detection accuracy and reduced storage footprint of the proposed model in comparison to prevailing alternatives. Overall, the approach delineated herein presents an efficacious and streamlined solution for real-time human security screening image recognition on resource-constrained devices, contributing a promising advancement in the field.

show abstract

Section: Lightweight Cnn Modelsmentioning

confidence: 99%

An Efficient Approach to Human Security Screening Image Recognition Through a Lightweight CNN Utilizing Yolov5s and GhostNet

Zhao,

Cheng

2023

View full text Add to dashboard Cite

show abstract

“…Deep learning (DL)-based neural networks have found extensive applications in biomedical sciences, including medical image segmentation and classification [13][14][15][16][17]. These techniques form crucial components of medical image analysis and are indispensable for monitoring and diagnosis applications.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Quality of Compressed Breast Ultrasound Imagery through Application of Wavelet Convolutional Neural Networks

Gencol,

Gungor

2023

View full text Add to dashboard Cite

Breast cancer, a pervasive and life-threatening malignancy, predominantly affects women worldwide. Despite the widespread adoption of imaging technologies such as mammography for early-stage breast cancer detection, access to such specialized imaging equipment remains limited in low-income countries. Conversely, ultrasound imaging has demonstrated its efficacy as a cost-effective tool for tumor identification. The advent of portable ultrasound devices facilitates rapid and precise lesion diagnosis in the breast, circumventing the need for hospital visits. Nevertheless, the images procured by portable ultrasound devices are typically necessitated to be transmitted in a compressed format for remote evaluation by physicians. This compression process often introduces artifacts in medical images, complicating the delineation of tumorous regions. To address this challenge, we introduce a deep-learning solution in this paper. A novel wavelet convolutional neural network (CNN) architecture is conceived to learn and subsequently diminish the artifacts present in compressed ultrasound images. To achieve this, a diverse dataset comprising various types of breast ultrasound imagesmalignant, benign, and normalis utilized. Experimental outcomes indicate that the proposed method surpasses the denoising CNN in mitigating artifacts in compressed ultrasound images. This improved performance is particularly evident in the most compressed images, which are of significant interest. This research underscores the potential of deploying deep-learning techniques to enhance the quality of compressed medical images, thereby facilitating more accurate and efficient remote diagnoses.

show abstract

Enhancing Plant Leaf Disease Prediction Through Advanced Deep Feature Representations: A Transfer Learning Approach

Naralasetti,

Bodapati

2024

J. Inst. Eng. India Ser. B

View full text Add to dashboard Cite

A Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans

Cited by 3 publications

References 21 publications

An Efficient Approach to Human Security Screening Image Recognition Through a Lightweight CNN Utilizing Yolov5s and GhostNet

An Efficient Approach to Human Security Screening Image Recognition Through a Lightweight CNN Utilizing Yolov5s and GhostNet

Enhancing the Quality of Compressed Breast Ultrasound Imagery through Application of Wavelet Convolutional Neural Networks

Enhancing Plant Leaf Disease Prediction Through Advanced Deep Feature Representations: A Transfer Learning Approach

Contact Info

Product

Resources

About