Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

Saravanan, S.; Kumar, Vipin; Velliangiri, S.; Indirajithu, Alagiri; Elangovan, D.; Allayear, Shaikh Muhammad

doi:10.1155/2022/4380901

Cited by 36 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This innovative approach yields results that are both objectively and subjectively compelling in terms of categorization. Validated using two datasets, BRATS and REMBRANDT, the proposed brain MRI classification algorithm surpasses the performance of existing methods 26 . Employing a multi-stage approach, the proposed method commences with preprocessing MRI images to eliminate noise and artifacts using an adaptive filter.…”

Section: Related Workmentioning

confidence: 92%

Employing deep learning and transfer learning for accurate brain tumor detection

Mathivanan,

Sonaimuthu,

Murugesan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

show abstract

Section: Related Workmentioning

confidence: 92%

Employing deep learning and transfer learning for accurate brain tumor detection

Mathivanan,

Sonaimuthu,

Murugesan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In the presented approach, a stretched convolutional neural network is combined with pre- and post-processing methods to enlarge the receptive field. As a result, big distance pixels in source images will help enrich the image features of the learned model, which effectively denoises the images [ 16 ]. The author proposes a lightweight, effective neural network-based raw image denoiser that works well on popular mobile devices and yields great-quality noise-removal results.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

Vimala

Srinivasan

Mathivanan

et al. 2023

Sensors

View full text Add to dashboard Cite

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

show abstract

“…The various methods currently employed to identify brain tumors are covered in this section. Saravanan et al [ 13 ] proposed that brain tumors could be automatically segmented and identified using a deep learning-based image analysis technique. This proposed a technology that is accurate and computationally effective for segmenting brain tumors.…”

Section: Related Workmentioning

confidence: 99%

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

et al. 2023

View full text Add to dashboard Cite

To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.

show abstract

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

Cited by 36 publications

References 28 publications

Employing deep learning and transfer learning for accurate brain tumor detection

Employing deep learning and transfer learning for accurate brain tumor detection

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

Contact Info

Product

Resources

About