Research Journal of Pharmacy and Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Comparative Study on Tumour Classification


Affiliations
1 Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India
2 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Rahul Kumar Sevakula, and Nishchal Kumar Verma. Assessing generalization ability of Majority Vote Point Classifiers, IEEE Transactions on Neural Networks and Learning Systems, 2017;28(12):1-13

  • Yunxiang Mao, Zhaozheng Yin and Joseph Schober. A Deep Convolutional Neural Network Trained on Representative Samples for Circulating Tumor Cell Detection, IEEE Winter Conference on Applications of Computer Vision (WACV), 2016:1-6

  • Amir Zjajo, Rene van Leuken, A 41 μW Real-Time Adaptive Neural Spike Classifier, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2016; 6(1):489-492

  • Ming-Chi Wu,Wen-Chi Chin and Ting-Chen Tsan. The Benign and Malignant Recognition System of Nasopharynx in MRI Image with Neural-Fuzzy based Adaboost Classifier, IEEE International Conference on Information Management (ICIM), 2016;5(4):1070-1074

  • Zhan-Li Sun, Chun-Hou Zheng, Qing-Wei Gao, Jun Zhang, and De-Xiang Zhang.Tumor. Classification Using Eigengene-Based Classifier Committee Learning Algorithm, IEEE signal processing letters, 2012;19(8):445-448.

  • K.S.Thara, K.Jasmine,Brain. Tumour Detection in MRI Images using PNN and GRNN, IEEE Conference International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016;l.6.(16):1504-1510.

  • Amsaveni.V, Albert Singh.N and Dheeba. J. Computer aided detection of tumor in MRI Brain images using cascaded correlation Neural network, IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON), 2013:12(4);527-532.

  • Wei Luo, Lipo Wang and Jingjing Sun. Feature Selection for Cancer Classification Based on Support Vector Machine, IEEE WRI Global Congress on Intelligent Systems,2009;5(9):422-426.

  • Khazendar,H.S Al-Assam,H. Du, S. Jassim ,A. Sayasneh, T. Bourne and J. Kaijser, and D. Timmerman. Automated Classification of Static Ultrasound Images of Ovarian Tumours Based on Decision Level Fusion, IEEE Computer Science and Electronic Engineering Conference (CEEC), 2014:9; 148-153.

  • Hemita Pathak Vrushali Kulkarni. Identification of Ovarian mass through Ultrasound Images using Machine Learning Techniques, IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), 2015:l.6;137-140.

  • Xiangyong Cao , Feng Zhou , Lin Xu, Deyu Meng ,Zongben Xu, and John Paisley. Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network, IEEE Transactions on Image Processing, 2018:27(5); 137-140.

  • Sarni Suhaila Rahim,Vasile Palade ,James Shuttleworth and Chrisina Jayne. Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing, Springer Brain Informatics journal, 2016:3( 4); 249–267.

  • Margarita Osadchy, Daniel Keren and Dolev Raviv. Recognition Using Hybrid Classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016:38(4); 1-30.

  • Robert Pike,Guolan Lu and Dongsheng Wang. A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection with Hyperspectral Imaging, IEEE Transactions on Biomedical Engineering, 2016:63(3); 1-11.

  • Chuan-Yu Chang, Hui-Ya Hu and Yuh-Shyan Tsai. Prostate Cancer Detection in Dynamic MRIs, IEEE International Conference on Digital Signal Processing (DSP), 2015:1(1); 1079-1282.

  • Fabio A. Spanhol, Luiz S. Oliveira, Caroline Petitjean, and Laurent Heutte. A Dataset for Breast Cancer Histopathological Image Classification,IEEE Transactions on Biomedical Engineering, 2016:63(7);1455-1462.

  • Argin Margoosian and Jamshid Abouei. Ensemble-based Classifiers for Cancer Classification Using Human Tumor Microarray Data,IEEE Iranian Conference on Electrical Engineering (ICEE), 2013:l(3);1-6.

  • Fengying Xie, Haidi Fan, Yang Li, Zhiguo Jiang, Rusong Meng, and Alan Bovik. Melanoma Classification on Dermoscopy Images Using a Neural Network Ensemble Model, IEEE Transactions on Medical Imaging, 2017: 36(3);1-6.

  • Shahriar Sazzad T.M, Armstrongand A L.J,Tripathy,K. An automated approach to detect human ovarian tissues using type P63 counter stained histopathology digitized color images, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI),2016:1;25-28.

  • Varuna Shree.N and T. N. R. Kumar. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network, Springer Brain Informatics journal,,2018:5(1);23–30

  • Ulagamuthalvi.V, Sridharan.D. Development of Diagnostic Classifier for Ultrasound Liver Lesion Images, International Journal of Computer Applications, 2012:.52(18); 12-15.

  • Noah Bedard,Mark Pierce, Adel El-Naggar, Anandasabapathy S, Ann Gillenwater and Richards-Kortum.R. Emerging roles for multimodal optical imaging in early cancer detection: a global challenge, Technology in Cancer Research and Treatment, 2010:9(2);1-6.


Abstract Views: 219

PDF Views: 0




  • A Comparative Study on Tumour Classification

Abstract Views: 219  |  PDF Views: 0

Authors

K. Srilatha
Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India
V. Ulagamuthalvi
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India

Abstract


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