Brain Tumor Classification Using Deep Neural Network and Transfer Learning

Kumar, Sandeep; Choudhary, Shilpa; Jain, Arpit; Singh, Karan; Ahmadian, Ali; Bajuri, Mohd Yazid

doi:10.1007/s10548-023-00953-0

Cited by 72 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The implementation of the proposed algorithm is evaluated by comparing the proposed with the SOTA methods like, Res-Net 50, Alex-Net, VGG-16 [27] and from the latest articles, given as Irmak et al [28], (2021) Kumar et al [11], (2023) Mohan et al [2] respectively.…”

Section: Comparative Methodsmentioning

confidence: 99%

“…The suggested system and current approaches, including Res-Net 50, Alex-Net, VGG-16, and those from recent works such as Irmak et al [28], (2021), Kumar et al [11], (2023), and Mohan et al [2], are compared in figure 11. The suggested flow provides best accuracy performance, as the figure illustrates.…”

Section: Comparative Methodsmentioning

confidence: 99%

“…Deep learning techniques-in particular, Convolutional Neural Networks have become a major field of study and research in an effort to overcome its shortcomings. In order to accurately classify brain tumors based on MRI scans, Kumar et al [11] present a unique CNN model incorporating transfer learning. Comparison tests with well-known pre-trained networks including Res-Net, Alex-Net, U-Net, and VGG-16 produced classification accuracy of 98.40%.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust brain tumor detection and classification via multi-technique image analysis

G R,

Jagadale

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

Accurate detection and classification of brain tumors play a critical role in neurological diagnosis and treatment.Proposed work developed a sophisticated technique to precisely identify and classify brain neoplasms in medical imaging. Our approach integrates various techniques, including Otsu’s thresholding, anisotropic diffusion, modified 3-category Fuzzy C-Means (FCM) for segmentation after skull stripping and wavelet transformation for post-processing for segmentation, and Convolution neural networks for classification. This approach not only recognizes that discriminating healthy brain tissue from tumor-affected areas is challenging, yet it also focuses on finding abnormalities inside brain tumors and early detection of tiny tumor structures. Initial preprocessing stages improve the visibility of images and the identification of various regions while accurately classifying tumor locations into core, edema, and enhancing regions by segmentation as well. Ultimately, these segmented zones are refined using wavelet transforms, which remove noise and improve feature extraction. Our CNN architecture uses learned abstractions to distinguish between healthy and malignant regions, ensuring robust classification. It is particularly good at identifying tiny tumors and detecting anomalies inside tumor regions, which provides substantial advances in accurate tumor detection. Comprehensive hypothetical evaluations validate its efficacy, which could improve clinical diagnostics and perhaps influence brain tumor research and treatment approaches.

show abstract

Section: Comparative Methodsmentioning

confidence: 99%

Section: Comparative Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust brain tumor detection and classification via multi-technique image analysis

G R,

Jagadale

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…Additionally, selected studies [57,118,119] explored the synergies of ensemble learning by combining the outputs of radiomics and DL models. Another interesting area of research has considered the opportunity of incorporating ML classifiers as the final layer in CNNs, effectively bypassing the traditional SoftMax layer [76,96,99,103,[119][120][121][122][123][124][125][126][127][128].…”

Section: Literature Reviewmentioning

confidence: 99%

Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

Pitarch,

Ungan,

Julià-Sapé

et al. 2024

Cancers

View full text Add to dashboard Cite

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.

show abstract

“…Sandeep Kumar et al 29 presented research work based on tumour detection in the brain using a CNN‐based transfer learning approach in MRI images. They used several pre‐trained models, such as Alex‐Net, U‐Net, VGG‐16 and Res‐Net.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis ofstate‐of‐the‐art CNNarchitectures for brain tumour detection

Khushi,

Masood,

Jaffar

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Deep learning models, such as convolutional neural network (CNN), are popular now a day to solve various complex problems in medical and other fields, such as image classification, object detection, recommendation of images, processing of natural languages and video and image analysis. So, the idea of studying the architecture of CNNs has gotten a lot of attention and become popular. This study analysed and contrasted the performance of many different CNN models trained on the publicly accessible Br35h dataset for the detection of brain tumours. These models included the LeNet, AlexNet, VGG16, VGG19 and ResNet50. Several optimisers were used in this research to fine‐tune the performance of the CNN model. These included Adam (adaptive moment estimation), SGD (stochastic gradient descent) and RMSprop (root‐mean‐square propagation). Accuracy, miss‐classification rate, sensitivity, specificity, NPV (negative predictive value), PPV (positive predictive value), F1‐score and false omission rate (FOR) were used to assess the efficacy of five different CNN architectures trained using three different optimisers. The experimental results showed that AlexNet architecture with SGD optimiser performed better than other CNN architecture with different optimisers and achieved the highest accuracy of 98.79% with a miss classification rate of 1.20%. It also achieved 98.98% sensitivity, 98.58% specificity, 98.93% NPV, 98.65% PPV, 98.82% F1‐score and 1.06% FOR.

show abstract

Brain Tumor Classification Using Deep Neural Network and Transfer Learning

Cited by 72 publications

References 12 publications

Robust brain tumor detection and classification via multi-technique image analysis

Robust brain tumor detection and classification via multi-technique image analysis

Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

Performance analysis ofstate‐of‐the‐art CNNarchitectures for brain tumour detection

Contact Info

Product

Resources

About