Deep Learning Based Image Classification and Abnormalities Analysis of MRI Brain Images

Krishnammal, P. Muthu; Raja, S. Selvakumar

doi:10.1109/imicpw.2019.8933239

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…Robust ML algorithms are exploited to classify brain tumour-related images (datasets) containing ANN, BPNN, SVM, K-NN, and PNN [12]. Deep learning (DL) is an emerging technology gaining popularity and widespread interest in all fields, especially in medical imaging analysis [13]. Deep learning improves flexibility and capability by analyzing irregular input over numerous layers.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Hybrid Approach for Brain Tumor Detection

Ullah

Ahmad

Shahzad

et al. 2023

PakJET

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Brain tumours are quickly increasing in prevalence all over the world. It causes the deaths of thousands of individuals annually. Misdiagnosis of brain tumours often results in unnecessary treatment, further lowering the survival rate of the affected individuals. Prompt medical diagnosis is crucial to improve the prognosis for patients with brain tumours. Positive advancements in deep and machine learning domains have been made due to repeated achievements in supporting medical practitioners in making correct diagnoses utilizing computer-aided diagnostic tools. Deep convolutional layers are superior to conventional methods at extracting unique characteristics from target regions. In this research, initially, Gabor filter and ResNet50 were applied to accurately extract the important features of brain tumours from the MRI images dataset. Firstly, the extracted features of Gabor and ResNet50 were classified individually through SVM, and secondly, the features from both these techniques were combined and then classified through SVM. The Kaggle MRI dataset for a brain tumour was utilized in this research. It includes 7,023 Images and four classes Glioma, Meningioma, No-Tumor, and Pituitary. The results from every system were outstanding, but the best results were shown by the combined features of Gabor and ResNet50, an advanced hybrid approach with 95.73% accuracy, 95.90% precision, and 95.72% f1 score.

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Section: Introductionmentioning

confidence: 99%