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Sathiaseelan, J.G.R.
- Texture Feature Extraction with Medical Image using Glcm and Machine Learning Techniques
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PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Bishop Heber College, IN
1 Department of Computer Science, Bishop Heber College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 4 (2022), Pagination: 2735-2740Abstract
Bones are a vital component of the human body. Bone provides the capacity to move the body. Humans have a high rate of bone fractures. The X-ray image is used by the doctors to identify the fractured bone. The manual fracture identification technique takes a long time and has a high risk of mistake. Machine learning and artificial intelligence are critical in resolving difficult difficulties in clinical imaging. Both medical practitioners and patients benefit from machine learning and artificial intelligence. Nowadays, an automatic system is built to detect abnormalities in bone X-ray pictures with great accuracy. To achieve high accuracy with limited resources, image pre-processing methods are employed to improve the quality of medical images. Image pre-processing entails steps such as noise removal and contrast enhancement, resulting in an instantaneous abnormality detection system. In image classification challenges, the Gray Level Co-occurrence Matrix (GLCM) texture features are commonly utilised. The second order statistical information about grey levels between nearby pixels in an image is represented by GLCM. In this work, we used various machine learning algorithms to categorise the MURA (musculoskeletal radiographs) dataset’s bone X-ray images into fractures and no fracture categories. For anomaly detection, the four different classifiers SVM (support vector machine), Random Forest, Logistic Regression, and Decision tree are utilised. The aforementioned abnormality detection in X-ray pictures is evaluated using five statistical criteria, including Sensitivity, Specificity, Precision, Accuracy, and F1 Score, all of which indicate considerable improvement.Keywords
Machine Learning, GLCM, SVM, Random Forest, Logistic Regression, Decision Tree, MURA, Bone Fractures
References
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- Prediction of Seed Purity and Variety Identification Using Image Mining Techniques
Abstract Views :73 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, Bishop Heber College, IN
1 Department of Computer Science, Bishop Heber College, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 4 (2022), Pagination: 2715-2722Abstract
Seed is a little embryonic plant that can be used to introduce plant infections to new areas while also allowing them to survive from one cropping season to the next. Seed health is a well-known component in modern agricultural science for achieving the required plant population and yield. Seed-borne fungus are a major biotic constraint in seed production around the world. The detection of seed-borne pathogens by seed health testing is a crucial step in the treatment of crop diseases. Speed and accuracy are critical requirements for long-term economic growth, competitiveness, and sustainability in agricultural output. Because human judgements in identifying objects and situations are variable, subjective, and delayed, seed prediction activities are costly and unreliable. Machine vision technology provides a nondestructive, cost-effective, quick, and accurate option for automated procedures. Seed variety, seed type (country seed or hybrid seed), seed health, and purity prediction were the four basic processes. We began the first procedure by aligning the seed bodies in the same direction using a seed orientation approach. Then, to detect atypical physical seed samples, a quality screening procedure was used. Their physical characteristics, such as shape, colour, and texture, were retrieved to serve as data representations for the prediction. This research introduces a new fuzzy cognitive map (FCM) model based on deep learning neural networks that predicts seed purity tests using data from biological investigations. The relevant data features from the seed test are extracted by FCM, which then effectively initialises the deep neural networks. The Levenberg–Marquardt (LM) technique for deep neural networks was discovered to improve seed purity test prediction. Four statistical machine learning algorithms (BP-ANN, Multivariate regression, and FCMLM deep learning). Furthermore, we demonstrated an improvement in the system's overall performance in terms of data quality, including seed orientation and quality screening. In independent numerical testing, the correlation coefficient between predicted values and true values acquired from experiments reached 0.9.Keywords
Fuzzy Cognitive Map, Deep Learning, FCMLM Deep Learning, BP-ANN, Multivariate Regression, Seed PurityReferences
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