Deep Learning with Unsupervised and Supervised Approaches in Medical Image Analysis

Geetha, G.; Thimmiaraja, J.; Shelke, Chetan J.; Pavithra, G.; Sharma, Vinay; Verma, Devvret

doi:10.1109/icacite53722.2022.9823491

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…It is formed by adding an additional hidden layer to the network model. In machine learning, we use DL as subfields to describe feature hierarchies [10]. This subfield builds on the demonstration of multiple levels of learning.…”

Section: Introductionmentioning

confidence: 99%

Hybrid model for detection of brain tumor using convolution neural networks

K.,

Dwarakanath

2024

Comput. Sci. Inf. Technol.

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The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.

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Section: Introductionmentioning

confidence: 99%