Brain MRI Classification using Deep Learning Algorithm

Kulkarni, Sunita M.; ,; Sundari, G.; ,

doi:10.35940/ijeat.c5350.029320

Cited by 5 publications

(6 citation statements)

References 18 publications

(23 reference statements)

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“…While CNN gives good results in face recognition for large databases, AlexNet can provide better results than classical CNN for small databases. [52]. Therefore, the pre-trained AlexNet CNN model was also utilized in the study.…”

Section: The Alexnet Classifiermentioning

confidence: 99%

Speaker identification using hybrid subspace, deep learning and machine learning classifiers

KESER,

GEZER

2024

Preprint

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Speaker identification is crucial in many application areas, such as automation, security, and user experience. This study examines the use of traditional classification algorithms and hybrid algorithms, as well as newly developed subspace classifiers, in the field of speaker identification. In the study, six different feature structures were tested for the various classifier algorithms. Stacked Features-Common Vector Approach (SF-CVA) and Hybrid CVA-FLDA (HCF) subspace classifiers are used for the first time in the literature for speaker identification. In addition, CVA is evaluated for the first time for speaker recognition using hybrid deep learning algorithms. This paper is also aimed at increasing accuracy rates with different hybrid algorithms. The study includes Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), i-vector + PLDA, Time Delayed Neural Network (TDNN), AutoEncoder + Softmax (AE + Softmaxx), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Common Vector Approach (CVA), SF-CVA, HCF, and Alexnet classifiers for speaker identification. The six different feature extraction approaches consist of Mel Frequency Cepstral Coefficients (MFCC) + Pitch, Gammatone Cepstral Coefficients (GTCC) + Pitch, MFCC + GTCC + Pitch + eight spectral features, spectrograms,i-vectors, and Alexnet feature vectors. For SF-CVA, 100% accuracy was achieved in most tests by combining the training and test feature vectors of the speakers separately. RNN-LSTM, i-vector + KNN, AE + softmax, TDNN, and i-vector + HCF classifiers gave the highest accuracy rates in the tests performed without combining training and test feature vectors.

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Section: The Alexnet Classifiermentioning

confidence: 99%

Speaker identification using hybrid subspace, deep learning and machine learning classifiers

KESER,

GEZER

2024

Preprint

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“…Another work focused on the classification and segmentation of tumors using pre-trained AlexNet, where features were extracted using the Gray-Level Co-Occurrence Matrix (GLCM) [24]. Other works include classification into different types of tumors using CNN [25][26][27][28][29], SVM [30], Graph cut [31], Recurrent Neural Network (RNN) [32,33], AlexNet transfer learning network of CNN [34], Deep Neural Network (DNN) [35][36][37], VGG-16, Inception V3 and ResNet50 [38], SVM and KNN [39], and CNN ensemble method [40].…”

Section: Related Workmentioning

confidence: 99%

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age

Wahlang

Maji

Saha

et al. 2022

Sensors

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Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.

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“…Many attempts have been made to investigate the value of transfer learning techniques for brain tumor classification [39,45,50,102,104,108,116,121]. Deepak and Ameer [39] used the GoogLeNet with the transfer learning technique to differentiate between glioma, MEN, and PT from the dataset provided by Cheng [55].…”

Section: Overview Of Included Studiesmentioning

confidence: 99%

“…Kulkarni and Sundari [121] utilized five transfer learning architectures, AlexNet, VGG16, ResNet18, ResNet50, and GoogLeNet, to classify benign and malignant brain tumors from the private dataset collected by the authors, which only contained 200 images (100 benign and 100 malignant). In addition, data augmentation techniques, including scaling, translation, rotation, translation, shearing, and reflection, were performed to generalize the model and to reduce the possibility of overfitting.…”

Section: Overview Of Included Studiesmentioning

confidence: 99%

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Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2021): Review, Challenges, and Future Perspectives

Xie¹,

Zaccagna²,

Rundo³

et al. 2022

Preprint

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Deep learning has shown remarkable results in every field, especially in the biomedical field, due to its ability to exploit large-scale datasets. A convolutional neural network (CNN) is a widely used deep learning approach to solve medical imaging problems. Over the past few years, many studies have focused on CNN-based techniques for brain tumor diagnosis. There are, however, still some critical challenges that CNNs face towards clinic application. This study presents a comprehensive review of current literature that involves CNN architectures for brain tumor classification. We compare the key achievements in the performance evaluation metrics of the applied classification algorithms. In addition, this review assesses the clinical effectiveness of the included studies to elaborate on the limitations and directions of this area for future work. No review focusing on the clinical effectiveness of previous works in this field has been published. We believe that this study has the potential to elevate the application of CNN-based deep learning methods in clinical practice and also can be a quick reference for biomedical researchers who are interested in this field.

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Brain MRI Classification using Deep Learning Algorithm

Cited by 5 publications

References 18 publications

Speaker identification using hybrid subspace, deep learning and machine learning classifiers

Speaker identification using hybrid subspace, deep learning and machine learning classifiers

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2021): Review, Challenges, and Future Perspectives

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