Modified Visual Geometric Group Architecture for MRI Brain Image Classification

Veni, N.; Manjula, J.

doi:10.32604/csse.2022.022318

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…The used VGG-16 model is represtented in the Figure 2. The more about VGG 16 is found in the reference [30], [31]…”

Section: Vgg-16mentioning

confidence: 99%

“…1) K-Nearest Neighour (K-NN) [25], [26] 2) Support Vector Machine (SVM) [27], [28] 3) Convolutional Neural Network (CNN) [29] 4) VGG-16 [30], [31] 5) GoogLeNet (Inception V1) [32], [33] 6) ResNet-50 [34], [35] The main intention of this study is to try and ascertain the level of model complexity required in order to achieve the best possible results for handwritten Devanagari digit classification. KNN, SVM and CNN are some of the most commonly used models for relatively simple image classification tasks such as in this case, Devanagari digit classification.…”

Section: Models Considered In This Studymentioning

confidence: 99%

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Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification

Gummaraju,

Shenoy,

Pai

2023

IEEE Access

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This work focuses on comparing the suitability of different machine learning models for the classification of handwritten digits in the Devanagari script. The models that will be compared in this study are: K-Nearest Neighbours (K-NN), Support Vector Machine (SVM), Convolutional Neural Network (CNN), GoogLeNet (Inception v1), and ResNet-50. GoogLeNet and ResNet-50 are complex, deep neural networks. They possess a large number of hidden layers, and are generally used for more complex image classification tasks. The use of these models in this project is to gauge how well they perform on simpler image data. The foundation of this research is based on the ever increasing demand for accurate and efficient digit classification models in India, for purposes such as document scanning, ID card recognition, and the digitization of institutional records. The primary objective of this research project is to identify the most accurate and efficient digit classification model for numbers in the Devanagari script. Surprisingly, proposed simple CNN model outperforms the other complex GoogleNet and ResNet-50 models. Accuracy and Fl score of proposed CNN model is 99.522% and 0.9978 respectively. Also, the proposed CNN model used in this study outperforms other CNN model considered for Devanagari numerals classification.

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“…The used VGG-16 model is represtented in the Figure 2. The more about VGG 16 is found in the reference [30], [31]…”

Section: Vgg-16mentioning

confidence: 99%

Section: Models Considered In This Studymentioning

confidence: 99%

Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification

Gummaraju,

Shenoy,

Pai

2023

IEEE Access

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“…Various studies have established that transfer learningbased CNN is better than traditional CNN training from scratch. Transfer learning is used in image classification [23], skin cancer diagnosis [24], brain cancer diagnosis [25], and lung cancer diagnosis [26]. Training from scratch suffers from computational overhead and complexity when larger datasets are involved.…”

Section: Transfer Learning Mechanismmentioning

confidence: 99%

Optoelectronic Retinal Images for the Prediction of Diabetic Macular Edema Based on a Hybrid Deep Transfer Learning Technique

Alavanthar,

Madheswaran

2024

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“…Finally, SVM is used for binary classification. Though different deep learning architectures [17][18][19] are developed for MRI brain image classification system, they suffer from high computation time, memory and also fine tuning of parameters is very difficult. The contribution of this research work is as follows:…”

Section: Introductionmentioning

confidence: 99%

Multi Class Brain Cancer Prediction System Empowered with BRISK Descriptor

Sahaai¹,

Jothilakshmi²,

Praveen³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Magnetic Resonance Imaging (MRI) is one of the important resources for identifying abnormalities in the human brain. This work proposes an effective Multi-Class Classification (MCC) system using Binary Robust Invariant Scalable Keypoints (BRISK) as texture descriptors for effective classification. At first, the potential Region Of Interests (ROIs) are detected using features from the accelerated segment test algorithm. Then, non-maxima suppression is employed in scale space based on the information in the ROIs. The discriminating power of BRISK is examined using three machine learning classifiers such as k-Nearest Neighbour (kNN), Support Vector Machine (SVM) and Random Forest (RF). An MCC system is developed which classifies the MRI images into normal, glioma, meningioma and pituitary. A total of 3264 MRI brain images are employed in this study to evaluate the proposed MCC system. Results show that the average accuracy of the proposed MCC-RF based system is 99.62% with a sensitivity of 99.16% and specificity of 99.75%. The average accuracy of the MCC-kNN system is 93.65% and 97.59% by the MCC-SVM based system.

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Modified Visual Geometric Group Architecture for MRI Brain Image Classification

Cited by 4 publications

References 18 publications

Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification

Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification

Optoelectronic Retinal Images for the Prediction of Diabetic Macular Edema Based on a Hybrid Deep Transfer Learning Technique

Multi Class Brain Cancer Prediction System Empowered with BRISK Descriptor

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