scholarly journals Image Processing Concepts for Brain Tumor MRI Image Classification

2020 ◽  
Vol 9 (10) ◽  
pp. 86-92
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Brain Tumor ◽  
Image Classification ◽  
Tumor Cells ◽  
Medical Image ◽  
Image Data ◽  
Medical Image Classification ◽  
Tumor Mri ◽  
Mri Image

The current generation is witnessing a radical change in technology with the rise of artificial intelligence. The application of artificial intelligence on different domain indicates the widespread involvement of this technology in the years to come. One such application is on medical image classification such as brain tumor classification. The process of medical image classification involves techniques from the image processing domain to process set of MRI image data in order to extract prominent feature that eases the classification process. The classifier model learns the MRI image data to predict the occurrence of the tumor cells. The objective of this paper is to provide knowledge pertaining to various approaches implemented in the field of machine learning applied to medical image classification as preparation of the MRI dataset to a standard form is the key for developing classifier model. the paper focus to analyses different types of preprocessing methods, image segmentation, and feature extraction methodologies and inscribes to points out the astute observation for each of techniques present in image processing methodologies. As predicting tumor cells is a challenging task because of its unpredictable shape. Hence emulating an appropriate methodology to improve the accuracy and efficiency is important as it aids in constructing a classifier model that can accelerate the process of prediction and classification for the brain tumor MRI imagery.

scholarly journals Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Brain Tumor ◽  
Image Classification ◽  
Medical Image ◽  
Data Augmentation ◽  
Medical Images ◽  
Medical Science ◽  
Vital Role ◽  
Tumor Classification

In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.

scholarly journals Implementation of Image Processing Technique for Diagnosis of Diseases

2018 ◽  
pp. 237-246
Author(s):  
Kalyani S. Boral ◽  
V. T. Gaikwad
Keyword(s):  
Image Processing ◽  
Image Classification ◽  
Medical Image ◽  
Image Data ◽  
Computer Aided Diagnosis ◽  
Image Processing Technique ◽  
Computer Aided ◽  
Medical Image Data ◽  
Aided Diagnosis ◽  
Diagnosis Of Diseases

Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages of diseases. Medical informatics is the study that combines two medical data sources: biomedical record and imaging data. Medical image data is formed by pixels that correspond to a part of a physical object and produced by imaging modalities. discovery of medical image data methods is a challenge in the sense of getting their insight value, analyzing and diagnosing of a specific disease. Image classification plays an important role in computer-aided-diagnosis of diseases and is a big challenge on image analysis tasks. This challenge related to the usage of methods and techniques in exploiting image processing result, pattern recognition result and classification methods and subsequently validating the image classification result into medical expert knowledge. The main objective of medical images classification is to reach high accuracy to identify the name of disease. It showed the improvement of image classification techniques such as to increase accuracy and sensitivity value and to be feasible employed for computer-aided-diagnosis are a big challenge and an open research.

scholarly journals Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 764
Author(s):  
Zhiwen Huang ◽  
Quan Zhou ◽  
Xingxing Zhu ◽  
Xuming Zhang
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Image Data ◽  
Classification Performance ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Triplet Loss ◽  
Medical Image Classification

In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classification performance. To improve the classification ability of light-weighted CNN models, we have proposed a novel batch similarity-based triplet loss to guide the CNNs to learn the weights. The proposed loss utilizes the similarity among multiple samples in the input batches to evaluate the distribution of training data. Reducing the proposed loss can increase the similarity among images of the same category and reduce the similarity among images of different categories. Besides this, it can be easily assembled into regular CNNs. To appreciate the performance of the proposed loss, some experiments have been done on chest X-ray images and skin rash images to compare it with several losses based on such popular light-weighted CNN models as EfficientNet, MobileNet, ShuffleNet and PeleeNet. The results demonstrate the applicability and effectiveness of our method in terms of classification accuracy, sensitivity and specificity.

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