Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification

Premamayudu, Bulla; Anantha, N Lakshmipathi; Subbarao, Peram

doi:10.18280/ts.370407

Cited by 22 publications

(11 citation statements)

References 20 publications

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“…The DNN is more popular because it is capable of extracting even complex features from large amount of data even from hours of speech data both Linear as well as non-linear because of their deeper architecture [17] as shown in Figure 6.…”

Section: Proposed Methodsmentioning

confidence: 99%

Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

Shivaprasad¹,

Sadanandam²

2021

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Dialect Identification is the process of identifies the dialects of particular standard language. The Telugu Language is one of the historical and important languages. Like any other language Telugu also contains mainly three dialects Telangana, Costa Andhra and Rayalaseema. The research work in dialect identification is very less compare to Language identification because of dearth of database. In any dialects identification system, the database and feature engineering play vital roles because of most the words are similar in pronunciation and also most of the researchers apply statistical approaches like Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), etc. to work on speech processing applications. But in today's world, neural networks play a vital role in all application domains and produce good results. One of the types of the neural networks is Deep Neural Networks (DNN) and it is used to achieve the state of the art performance in several fields such as speech recognition, speaker identification. In this, the Deep Neural Network (DNN) based model Multilayer Perceptron is used to identify the regional dialects of the Telugu Language using enhanced Mel Frequency Cepstral Coefficients (MFCC) features. To do this, created a database of the Telugu dialects with the duration of 5h and 45m collected from different speakers in different environments. The results produced by DNN model compared with HMM and GMM model and it is observed that the DNN model provides good performance.

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Section: Proposed Methodsmentioning

confidence: 99%

Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

Shivaprasad¹,

Sadanandam²

2021

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“…CNN was realized as the first real-world application in 1998 to observe handwritten digits [ 10 ]. Premamayudu Bulla et al [ 14 ] developed a hybrid model based on CNN for classifying the tumor type in the brain. Hassan Ali Khan et al [ 7 ] proposed an automated brain tumor detection mechanism applying CNN with transfer learning models on the MRI brain image dataset.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images

Srinivas¹,

S.²,

Zakariah

et al. 2022

Journal of Healthcare Engineering

154

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Brain tumor classification is a very important and the most prominent step for assessing life-threatening abnormal tissues and providing an efficient treatment in patient recovery. To identify pathological conditions in the brain, there exist various medical imaging technologies. Magnetic Resonance Imaging (MRI) is extensively used in medical imaging due to its excellent image quality and independence from ionizing radiations. The significance of deep learning, a subset of artificial intelligence in the area of medical diagnosis applications, has macadamized the path in rapid developments for brain tumor detection from MRI to higher prediction rate. For brain tumor analysis and classification, the convolution neural network (CNN) is the most extensive and widely used deep learning algorithm. In this work, we present a comparative performance analysis of transfer learning-based CNN-pretrained VGG-16, ResNet-50, and Inception-v3 models for automatic prediction of tumor cells in the brain. Pretrained models are demonstrated on the MRI brain tumor images dataset consisting of 233 images. Our paper aims to locate brain tumors with the utilization of the VGG-16 pretrained CNN model. The performance of our model will be evaluated on accuracy. As an outcome, we can estimate that the pretrained model VGG-16 determines highly adequate results with an increase in the accuracy rate of training and validation.

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“…Deep learning CNN (DCNN) models contain series of layers with filters to perform feature extraction and dimensional reduction. The thin layers of a deep CNN method for visual acknowledgment adopts low-level features like edges, though the deeper layers adapt more semantical ideas by consolidating lower-level features [26,27].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Age Estimation from Left-Hand Radiographs with Deep Learning Methods

Özdemir¹,

Gedik²,

Kaya³

2021

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Bone age is estimated in pediatric medicine for medical and legal purposes. In pediatric medicine, it aids in the growth and development assessment of various diseases affecting children. In forensic medicine, it is required to determine criminal liability by age, refugee age estimation, and child-adult discrimination. In such cases, radiologists or forensic medicine specialists conduct bone age estimation from left hand-wrist radiographs using atlas methods that require time and effort. This study aims to develop a computer-based decision support system using a new modified deep learning approach to accelerate radiologists' workflow for pediatric bone age estimation from wrist radiographs. The KCRD dataset created by us was used to test the proposed method. The performance of the proposed modified IncepitonV3 model compared to IncepitonV3, MobileNetV2, EfficientNetB7 models. Acceptably high results (MAE=4.3, RMSE=5.76, and R2=0.99) were observed with the modified IncepitonV3 transfer deep learning method.

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Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification

Cited by 22 publications

References 20 publications

Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

Dialect Identification in Telugu Language Speech Utterance Using Modified Features with Deep Neural Network

Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images

Age Estimation from Left-Hand Radiographs with Deep Learning Methods

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