Design of Automated Computer-Aided Classification of Brain Tumor Using Deep Learning

2019 ◽  
pp. 285-291
Author(s):  
Nur Alisa Ali ◽  
A. R. Syafeeza ◽  
Liow Jia Geok ◽  
Y. C. Wong ◽  
Norihan Abdul Hamid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Computer Aided

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

Classification of Brain Tumor Images Using Enhanced Deep Learning-Based Methodologies

2021 ◽  
pp. 519-532
Author(s):  
Aakash Paul ◽  
Priyanshi Chauhan ◽  
Himansh Sharma ◽  
Kartikay Khosla ◽  
Varun Srivastava ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor

scholarly journals Hybrid Architecture for Brain Abnormality Detection on brain MR Scans via FastAI

2021 ◽  
Author(s):  
Asmita Dixit
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Transfer Learning ◽  
Tumor Classification ◽  
Tumor Segmentation ◽  
Hybrid Architecture ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Brain Abnormality ◽  
Computer Aided

Abstract With lot happening in the field of Deep Learning, classification of brain tumor is still a matter of concern. Brain tumor segmentation and classification using MRI scans has achieved lot of interest in the area of medical imaging. The emphasis still lies on developing automatic computer-aided system for early predictions and diagnosis. MRI of brain Tumors not only varies in shape but sometimes gives less contrasted details also. In this paper, we present a FastAI based Transfer Learning tumor classification in which pre-trained model with segmented features classifies tumor based on its learning. The proposed model with the technique of Deep learning applies ResNet152 as base model to extract features from the MRI brain images. With certain changes in the last 3 layers of ResNet152, 97% accuracy in Dataset-253, 96% accuracy in Dataset-205 is achieved. Models such as Resnet50, VGG16, ResNet34 and Basic CNN is also evaluated. The model improved from ResNet152 has provided improved results. The observations suggest that usage of Transfer Learning is effective when the Dataset is limited. The prepared model is effective and can be collaborated in computer-aided brain MR images Tumor classification.

scholarly journals Classification of brain tumor isocitrate dehydrogenase status using MRI and deep learning

2019 ◽  
Vol 6 (04) ◽  
pp. 1
Author(s):  
Sahil Nalawade ◽  
Gowtham K. Murugesan ◽  
Maryam Vejdani-Jahromi ◽  
Ryan A. Fisicaro ◽  
Chandan G. Bangalore Yogananda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Isocitrate Dehydrogenase

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

2018 ◽  
Author(s):  
Sarah Aboutalib ◽  
Aly Mohamed ◽  
Margarita Zuley ◽  
Wendie Berg ◽  
Shandong Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Computer Aided

scholarly journals Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5551
Author(s):  
Chan-Il Kim ◽  
Seok-Min Hwang ◽  
Eun-Bin Park ◽  
Chang-Hee Won ◽  
Jong-Ha Lee
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Malignant Melanoma ◽  
Skin Lesion ◽  
Diagnostic Algorithm ◽  
The United States ◽  
Classification Model ◽  
Benign Tumors ◽  
Computer Aided

Malignant melanoma accounts for about 1–3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.

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