Brain Tumor Detection and Classification using Magnetic Resonance Imaging and Machine Learning Approaches

Sree, B. Ramya; Rao, N. V. Subba; Srinivasulu, Ch; Kumar, Turpati Pavan; Ramaneesh, Valisetty; Narayana, J.S. Bharath; Dutta, Sourav

doi:10.1109/iccmc53470.2022.9754077

Cited by 2 publications

(1 citation statement)

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“…Amongst the presented imaging technologies, BT is considerably identified using Magnetic Resonance Imaging (MRI) without medicinal brain surgery [3]. The MRI system is considered a non-invasive medical imaging technique and is pain-free which attains high-resolution images of tumours.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Driven Computer-Aided Diagnosis Model for Brain Tumor Classification on Magnetic Resonance Imaging

Vaiyapuri,

Mahalingam,

Ahmad

et al. 2023

IEEE Access

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Brain tumour (BT) detection involves the process of identifying the presence of a brain tumour in medical imaging, such as MRI scans. BT detection often relies on medical imaging techniques, such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), or PET (Positron Emission Tomography) scans. Early detection of BT is important and MRI is one of the primary imaging techniques used to diagnose and assess BT. Deep learning (DL) techniques, particularly convolutional neural networks (CNNs) have shown promising results in assisting with BT detection on MRI scans. This study designs an Ensemble Learning Driven Computer-Aided Diagnosis Model for Brain Tumor Classification (ELCAD-BTC) technique on MRIs. The presented system purposes to detect and classify various steps of BTs. The presented system contains a Gabor filtering (GF) approach to remove the noise and increase the quality of MRI images. Moreover, ensemble learning of three DL models namely EfficientNet, DenseNet, and MobileNet is utilized as feature extractors. Furthermore, the denoising autoencoder (DAE) approach can be exploited to detect the presence of BTs. Finally, a social spider optimization algorithm (SSOA) was carried out for the hyperparameter tuning of the DL models. For simulating the improved BT classification outcome, a brief set of simulations occur on BRATS 2015 database.

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Section: Introductionmentioning

confidence: 99%