High-performance compression-based brain tumor detection using lightweight optimal deep neural network

Kumar, B.P. Santosh; Shaik, Shafiulla Basha; Mulam, Harikrishna

doi:10.1016/j.advengsoft.2022.103248

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…The focus extends to abnormal brain growth Segmentation, a critical medical image analysis task involving the separation of abnormal brain growth from normal brain tissue in imaging scans. 13 Leveraging convolutional neural networks (CNN) proves advantageous, as they autonomously learn intricate features from multi-modal brain images, enhancing accuracy. This approach not only automates image processing but also contributes to the refined identification of both healthy and abnormal brain growth tissues.…”

Section: Literature Surveymentioning

confidence: 99%

Frequent CNN based ensembling for MRI classification for Abnormal Brain Growth detection

Bag,

Patil,

Nagnath Kendre

2024

JIST

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Digital image processing is a key player in the analysis of medical images, particularly in understanding the intricacies of abnormal brain growth development. Notably, the application of CNN algorithms to MRI images accelerates abnormal brain growth detection with enhanced accuracy; facilitating prompt decision-making by radiologists. This research focuses on finding abnormal brain growth using advanced CNN computer techniques. The study is split into three main steps. In the first step, brain MRI images are pre-processed by applying selected pre-processing techniques. In the second step, machine learning feature extraction methods are applied to pick out important features from these images. Finally, CNN models such as VGG, ResNet, DenseNet, and MobileNet are applied to classify the MRI images at a detailed level. The ensemble is done to improve the accuracy of the classification of MRI images. The results from study indicate easy automated abnormal brain growth detection that save radiologists' time and improve the efficiency of early diagnosis.

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Section: Literature Surveymentioning

confidence: 99%

Frequent CNN based ensembling for MRI classification for Abnormal Brain Growth detection

Bag,

Patil,

Nagnath Kendre

2024

JIST

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“…The motivation for studying medical image compression stems from the need to reduce storage space, improve image transmission speed, secure sensitive patient information transmission, and enable real-time access to the images for efficient patient care. In addition, developing efficient compression techniques will significantly impact the healthcare industry by improving the overall management and accessibility of medical images, thereby aiding in the early diagnosis and treatment of various medical conditions [24]. As the volume of medical images increases, the need for efficient compression methods will become even more critical.…”

Section: Medical Image Compressionmentioning

confidence: 99%

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

Aziz,

Mohd Sam,

Hassan

et al. 2023

IEEE Access

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In this modern era, medical image sharing has become a routine activity within hospital information systems. Digital medical images have become valuable resources that aid health care systems' decision-making and treatment procedures. A medical image consumes a significant amount of memory, and the size of medical images continues to grow as medical imaging technology progresses. In addition, an image is shared for analysis to support knowledge sharing and disease diagnosis. Therefore, health care systems must ensure that medical images are appropriately distributed without information loss in a timely and secure manner. Image compression is the primary process performed on each medical image before it is shared to ensure that the purpose of sharing an image is accomplished. The hybrid region of interest-based medical compression algorithms reduces image size. Furthermore, these algorithms shorten the image compression process time by manipulating the advantages of lossy and lossless compression techniques. A comprehensive review of previous studies that utilized this approach was conducted. Sample studies were selected from published articles in an open database subscribed to by Universiti Teknologi Malaysia for ten years (2012 to 2023). This work aims to critically review and comprehensively analyze previous types of algorithms by focusing on their main performance results: compression ratio, mean square error and peak signal-to-noise ratio. This article will identify which type of algorithm can give optimal value to the primary performance metric for compressing medical images. INDEX TERMS DICOM, Hybrid Image Compression, Medical Image, Performance Review, Region of Interest-based, I. INTRODUCTIONMedical images are critical resources that assist physicians and facilitate monitoring of a patient's health condition, providing a wide range of information that doctors and specialists need to make accurate diagnoses. Moreover, medical images are among the most critical assets of a health care information system (HIS). Health care practitioners utilize these images to electronically detect, diagnose, treat and evaluate various diseases.In the current technological era, image files occupy a significant amount of memory. The same issue applies to medical images. Each medical image consumes a vast amount of memory and storage space. Therefore, an HIS administrator must ensure sufficient memory space for storing medical images. In addition, the records of many patients must be retained for an extended period, consuming more space for storing images [1], [2].The primary operation that involves medical images in an HIS is medical image sharing. With the challenges that we are currently facing due to the Coronavirus disease 2019 (COVID-19) pandemic, the need for image sharing has become particularly evident as movement is restricted in many places worldwide. In many clinical cases, diagnostic planning requires collaboration between referring physicians

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Efficient brain tumour detection system by Cascaded Fully Convolutional Improved DenseNet with Attention-based Adaptive Swin Unet-derived segmentation strategy

Vinisha,

Boda

2024

Multimedia Systems

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High-performance compression-based brain tumor detection using lightweight optimal deep neural network

Cited by 7 publications

References 19 publications

Frequent CNN based ensembling for MRI classification for Abnormal Brain Growth detection

Frequent CNN based ensembling for MRI classification for Abnormal Brain Growth detection

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

Efficient brain tumour detection system by Cascaded Fully Convolutional Improved DenseNet with Attention-based Adaptive Swin Unet-derived segmentation strategy

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