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Srilatha, K.
- A Comparative Study on Tumour Classification
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, IN
2 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, IN
1 Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, IN
2 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 1 (2019), Pagination: 407-411Abstract
Cancer detection is the most significant method to identify the early tumor. Enlargement of the tumor is being a huge task due to the complex characteristics of the medical images which provides high divergent, intensive and uncertain boundaries. Designing and developing computer-aided image processing systems are to help doctors improve their diagnosis and then received huge benefits over the past years. Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories that includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification. Many classification techniques have been developed for image classification. The aim of literature survey is to provide a brief summary about some of common most image classification technique and comparison among them. In this survey various classification techniques are considered; Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), Fuzzy Classification and more.Keywords
Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), Fuzzy Classification.
References
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- Detection of AML in Blood Microscopic Images using Local Binary Pattern and Supervised Classifier
Abstract Views :167 |
PDF Views:0
Authors
Affiliations
1 ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, IN
1 ECE Department, Sathyabama Institute of Science and Technology, Chennai-119, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 4 (2019), Pagination: 1717-1720Abstract
A novel method of detecting Acute myelogenous leukemia (AML) disease using image processing algorithms is discussed in this paper. AML is an high risk disease which should be diagnosed early. AML detection is challenging, and should be performed by a qualified hematopathologist or hematologist. This paper discusses an automatic detection of AML using image processing methods. The algorithm consists contrast enhancement, Local binary pattern detection and Fuzzy C mean clustering technique. This Automatic detection method will helps the hematologists for easier identification and early detection of leukemia from blood microscopic images which will improve the chances of survival for the patient. A fuzzy based two stage color segmentation strategy is employed for segregating leukocytes or white blood cells (WBC) from other blood components. Discriminative features i.e. nucleus shape, texture are used for final detection of leukemia.Keywords
Acute Myelogenous Leukemia, Fuzzy C Mean Clustering, Local Binary Pattern Feature Extraction.References
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