Book ChapterDOI
Automatic X-ray Image Classification System
C. M. A. K. Zeelan Basha,T. Maruthi Padmaja,G. N. Balaji,G. N. Balaji +3 more
- pp 43-52
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TLDR
An attempt has been made and a system which involves five image processing steps namely, denoising using high boost filter, enhancement using adaptive histogram equalization, statistical feature extraction, and classification using artificial neural network can be used as an effective tool for X-ray image classification.Abstract:
In recent days, computer-aided fracture detection system plays a role in aiding both orthopaedician and a radiologist by providing accurate and fast results. In order to detect the fracture automatically, classification of X-ray images should be automated and it becomes the initial step. Therefore, an attempt has been made and a system is presented in this paper, which involves five image processing steps namely, denoising using high boost filter, enhancement using adaptive histogram equalization, statistical feature extraction, and classification using artificial neural network. To classify the given input X-ray images into the categories head, neck, skull, foot, palm, and spine, the probabilistic neural network, backpropagation neural network, and support vector machine classifiers are employed in classifying X-ray images. The results ascertain an overall accuracy of 92.3% in classifying X-ray images and the presented system can be used as an effective tool for X-ray image classification.read more
Citations
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Proceedings ArticleDOI
Enhanced Computer Aided Bone Fracture Detection Employing X-Ray Images by Harris Corner Technique
TL;DR: Harris corner based detection algorithm is proposed to extract features from the image and the extracted features from this algorithm can identify edges, fractures and corners present in the image.
Proceedings ArticleDOI
Automatic Human Emotion Recognition System using Facial Expressions with Convolution Neural Network
TL;DR: An automatic facial emotion classification system is proposed in this article using the convolution neural network (CNN) with the features extracted from the Speeded Up Robust Features (SURF). 91% accuracy is achieved with the proposed model which supports tracking human emotion with facial expressions.
Proceedings ArticleDOI
Advanced Prediction of Performance of a Student in an University using Machine Learning Techniques
TL;DR: Here the performance of K L University students is evaluated by applying all the above algorithms, which were combined and used for student evaluation used for recruiting process.
Proceedings ArticleDOI
Enhanced and Effective Computerized Multi Layered Perceptron based Back Propagation Brain Tumor Detection with Gaussian Filtering
TL;DR: An automated Tumor detection technique is proposed which aids neurosurgeons in detecting brain tumors with an accuracy rate of 93% when compared with other Classifiers like PNN (Probabilistic Neural Network) and SVM (Support Vector machine).
Journal ArticleDOI
Fractured Elbow Classification Using Hand-Crafted and Deep Feature Fusion and Selection Based on Whale Optimization Approach
TL;DR: The proposed method’s performance is evaluated on 16,984 elbow X-ray radiographs taken from the publicly available musculoskeletal radiology (MURA) dataset and provides 97.1% accuracy and a kappa score of 0.943% for the classification of elbow fractures.
References
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