Tumor Classification using Block wise fine tuning and Transfer learning of Deep Neural Network and KNN classifier on MR Brain Images

Anilkumar, B

doi:10.30534/ijeter/2020/48822020

Cited by 21 publications

(2 citation statements)

References 20 publications

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“…However, acquiring such datasets can be challenging. To tackle this issue, a transfer learning approach with VGGnet is presented in [7], specifically designed for small medical datasets. The authors fine-tuned blocks of the VGGnet architecture and created six different models to assess their impact on two publicly available datasets (BraTS and MRI).…”

Section: Literature Surveymentioning

confidence: 99%

A Survey of Techniques for Brain Anomaly Detection and Segmentation Using Machine Learning

Narayanan¹,

Latifi²

2023

IJCNS

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In this research report, various Machine Learning (ML) models are discussed for the purpose of detecting brain anomalies like tumors. In the first step, we review previous work that uses Deep Learning (DL) to classify and detect brain tumors. Next, we present a detailed analysis of the ML methods in tabular form to address the brain tumor morphology, accessible datasets, segmentation, extraction, and classification using DL, and ML models. Finally, we summarize all relevant material for tumor detection, including the merits, limitations and future directions. In this study, it is found that employing DL-based and hybrid-based metaheuristic approaches proves to be more effective in accurately segmenting brain tumors, compared to the conventional methods. However, the brain tumor segmentation using ML models suffers from drawbacks due to limited labelled data, variability in tumor appearance, computational memory requirements, transparency in models, and difficulty in integration into clinical workflows. By pursuing techniques such as Data Augmentation, Pre-training, Active-learning, Multimodal fusion, Hardware acceleration, and Clinical integration, researchers and developers can overcome the bottlenecks and enhance the accuracy, efficiency, and clinical utility of ML-based brain tumor segmentation models.

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Section: Literature Surveymentioning

confidence: 99%

A Survey of Techniques for Brain Anomaly Detection and Segmentation Using Machine Learning

Narayanan¹,

Latifi²

2023

IJCNS

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“…2 shows the samples of the datasets gathered which were already cropped. To construct a network that can properly classify the image to its corresponding character using a convolutional neural network, we used Keras and a Convolutional Neural Network architecture (VGG16) [17], [18] containing group of unlike layers for handing out of training of data.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Baybayin (Ancient Philippine Character) Handwritten Letters Using VGG16 Deep Convolutional Neural Network Model

2020

IJETER

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We proposed a system that can convert 45 handwritten baybayin Philippine character/s into their corresponding Tagalog word/s equivalent through convolutional neural network (CNN) using Keras. The implemented architecture utilizes smaller, more compact type of VGG16 network. The classification used 1500 images from each 45 baybayin characters. The pixel values resulting from the resized characters (50x50 pixels) of the segmentation stage have been utilized for training the system, thus, achieving a 99.54% accuracy. To test the developed recognition system, 90 handwritten baybayin characters were captured in real time using its 1080P Full-HD web camera. Next, the system will classify the test sample. Lastly, the corresponding Tagalog word output is shown on the screen for the user. The overall accuracy for the testing phase is 98.84%. As a result, the proposed system will find possible applications in character extraction in documents and any related translation of handwritten document to structural text form.

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Potential roles of transformers in brain tumor diagnosis and treatment

Lan

Zou

Qin

et al. 2023

Brain-X

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Brain tumor (BT) is one of many malignancies that have substantially enhanced global human morbidity and mortality rates. Early detection and characterization of glioma are essential for effective preventive strategies. Currently, the use of Transformers, a deep learning model for BT diagnosis and treatment, is attracting significant attention. The transformer self‐attention mechanism automatically learns the associations between input data for efficient processing and analysis. Research indicates that Transformers could play an essential role in the BT segmentation of magnetic resonance imaging (MRI) images, the MRI and histopathology‐based grading of brain cancer, BT molecular expression prediction, the classification of primary brain metastasis sites, voxel‐level dose and BT radiotherapy outcome prediction, synergistic prediction, and the pathway deconvolution of drug combinations. In this review, the feasibility, accuracy, and applicability of various algorithms are systematically analyzed and their prospects are discussed. Overall, this review aimed to discuss and provide an overview of the increasing applications of Transformers in real‐time BT detection and therapy, indicating their broad prospects and potential. In the future, Transformers are expected to be increasingly used for the diagnosis and subsequent treatment of BT because of the continuous development and improvement of Transformer‐based deep learning technology. However, more work is required to investigate their properties for anomaly detection, medical image classification, network design development, and application to other medical data.

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Tumor Classification using Block wise fine tuning and Transfer learning of Deep Neural Network and KNN classifier on MR Brain Images

Cited by 21 publications

References 20 publications

A Survey of Techniques for Brain Anomaly Detection and Segmentation Using Machine Learning

A Survey of Techniques for Brain Anomaly Detection and Segmentation Using Machine Learning

Recognition of Baybayin (Ancient Philippine Character) Handwritten Letters Using VGG16 Deep Convolutional Neural Network Model

Potential roles of transformers in brain tumor diagnosis and treatment

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