Artificial Intelligent Systems and Machine Learning

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

Design of a Classifier System using Machine Learning Techniques for Diffuse Goiter Diagnosis


Affiliations
1 Department of BCA, RRIT, Bangalore, India
2 Department of MCA, SIT, Tumkur, India
     

   Subscribe/Renew Journal


A Diffuse goiter is usually an auto immune disease where the thyroid gland situated in the neck becomes enlarged. It is one of the most common thyroid disorders. In this paper, a diagnostic classifier system is designed to diagnose diffuse goiter using Thyroid Ultrasound (TUS) images.  Normal and goiter TUS images are pre-processed using Matlab software to extract Region of Interest (ROI). Relevant features were extracted from ROI. These features are validated by classifier system using different machine learning techniques- Naïve Bayes, Multilayer Perceptron (MLP), Radial Basis Function (RBF), Instance Based K-nearest neighbor (IBK) and J-48. The performance evaluation of the developed system is estimated using classification accuracy, time taken to build the model, Mean Absolute Error (MAE), Kappa Statistic and ROC Area. The obtained classification accuracies of different classifiers are in the range 88%-93%, 85%-91% and 83%-90% using 3-fold, 10-fold and hold-out (60%-40%) methods respectively. The results are very promising, indicating selected features were robust for diagnosis of diffuse goiter and also demonstrating the utility of machine learning techniques in medical domain.


Keywords

Machine Learning, Image Processing, Diffuse Goiter, Pre-Processing, Feature Extraction, Classification.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Shijun Wang, Ronald M. Summers, “Machine Learning and Radiology”, Medical Image Analysis, Vol. 16, No. 05, pp. 933-951, doi:10.1016/j.media.2012.02.005, 2012.

  • Marjan Laal, “Innovation Process in Medical Imaging”, Procedia - Social and Behavioral Sciences, Vol. 81, pp. 60 – 64, 2013.

  • Marc Kohli, Luciano M orevedello, Ross W. Filice, J. Raymond Geis, “Implementing Machine Learning in Radiology Practice and Research”, Americal Journal of Roentgenology, Vol. 208, No. 04, pp.754-760, doi:10.2214/AJR.16.17224, 2017.

  • James H.Thrall, XiangLi, QuanzhengLi, CinthiaCruz, SynhoDo, KeithDreyerDO, JamesBrink, “Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success”, Journal of the American College of Radiology, Vol. 15, No. 3, pp. 504-508, doi.org/10.1016/j.jacr.2017.12.026, 2018.

  • Erickson B J, Korfiatis P, Akkus Z, Kline T L, “Machine Learning for Medical Imaging. Radiographics”, .Vol. 37, No. 02, pp.505-515, doi: 10.1148/rg.2017160130, 2017.

  • Asha Gowda Karegowda, Poornima.D, Lakshmi C.R, “Knowledge Based Fuzzy Inference System for Diagnosis of Diffuse Goiter”, 2nd International Conference on Emerging Computation and Information Technologies (ICECIT), 10.1109/ICECIT.2017.8453338, 2017.

  • www.medicinenet.com/thyroid-disorders (accessed on 21st October 2018).

  • Poornima. D, Asha Gowda Karegowda, “A Pragmatic Review of Data Mining Techniques for Thyroid Categorization “, Alloy Journal of Soft Computing Applications, Vol. 03, No. 01, pp. 24-46, 2015.

  • www.healthline.com/health/guide/subclinical-hypothyroidism/ (accessed on 2nd October 2018).

  • World Health Organization, www.who.int/diagnostic_imaging/ (accessed on 10th october 2018)

  • American Thyroid Association, www.thyroid.org/goiter/(accessed on 21st October 2018).

  • www.mayoclinic.org/diseases-conditions/goiter/symptoms-causes/ (accessed on 30th October 2018).

  • Banu Diri and Songul Albayrak, “Visualization and analysis of classifiers performance in multi-class medical data”, Expert Systems with Applications, vol. 34, no. 1, pp. 628-634, 2008.

  • M. Malathi, S. Srinivasan, “Classification of Ultrasoud Thyroid Nodule Using Feed Forward Neural Network”, World Engineering & Applied Sciences Journal, Vol. 08, No. 01, pp.12-17, 2017.

  • Shaik Razia, M.R.Narasingarao, “A Neuro Computing Frame Work for Thyroid Disease Diagnosis Using Machine Learning Techniques”, Journal of Theoretical and Applied Information Technology, Vol. 95, No. 09, pp. 1996-2005, 2017.

  • Prabal Poudel , Alfredo Illanes, Debdoot Sheet, Michael Friebe, “Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches”, Hindawi Journal of Healthcare Engineering, Vol. 2018, Article ID 8087624, doi.org/10.1155/2018/8087624.

  • Deepika Koundal, Savita Gupta, Sukhwinder Singh, “Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set”, Applied Soft Computing, Vol. 40, pp. 86-97, 2016.

  • Feyzullah Temurtas, “A Comparative Study on Thyroid Disease Diagnosis using Neural Networks”, Expert Systems with Applications, vol. 36, pp. 944-949, 2009.

  • Hui-Ling Chen, Bo yang, Gang Wang, Jie Liu, Yi-Dong Chen and Da-You-Liu, “A Three-Stage Expert System based on Support Vector Machines for Thyroid Disease Diagnosis”, Journal of Medical Systems, vol. 36, pp. 1953-1963, 2012.

  • https://www.mathworks.com

  • Vikas Chaudhary, Shahina Bano, “Imaging of the thyroid: Recent advances”, Indian Journal of Endocrinology and Metabolism, Vol. 16, No. 03, pp. 371-376, DOI: 10.4103/2230-8210.956742012.

  • Poornima .D, Asha Gowda Karegowda, P.T.Bharathi, “A Pragmatic Review of Denoising Techniques applied for Medical Images”, International Journal of Applied Research on Information Technology and Computing, Vol. 08, No. 01, pp.81-104, 2017.

  • Şaban Öztürka, Bayram Akdemir, “Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA”, Procedia Computer Science, Vol. 132, pp. 40-46, 2018.

  • Sarah N.Abdulkader,AymanAtia, Mostafa-Sami M, Mostafa, “Brain computer interfacing: Applications and challenges”, Egyptian Informatics Journal, Vol. 16, No. 02, Pages 213-230, 2015.

  • Ian H. Witten, Eibe Frank, “Data Mining- Practical Machine Learning Tools and Techniques”, Morgan Kaufmann Publishers, II edition, 2005.

  • Markov, Zdravko, and Ingrid Russell. "An introduction to the WEKA data mining system" ACM SIGCSE Bulletin. Vol. 38. No.03, 2006.


Abstract Views: 333

PDF Views: 1




  • Design of a Classifier System using Machine Learning Techniques for Diffuse Goiter Diagnosis

Abstract Views: 333  |  PDF Views: 1

Authors

D. Poornima
Department of BCA, RRIT, Bangalore, India
Asha Gowda Karegowda
Department of MCA, SIT, Tumkur, India

Abstract


A Diffuse goiter is usually an auto immune disease where the thyroid gland situated in the neck becomes enlarged. It is one of the most common thyroid disorders. In this paper, a diagnostic classifier system is designed to diagnose diffuse goiter using Thyroid Ultrasound (TUS) images.  Normal and goiter TUS images are pre-processed using Matlab software to extract Region of Interest (ROI). Relevant features were extracted from ROI. These features are validated by classifier system using different machine learning techniques- Naïve Bayes, Multilayer Perceptron (MLP), Radial Basis Function (RBF), Instance Based K-nearest neighbor (IBK) and J-48. The performance evaluation of the developed system is estimated using classification accuracy, time taken to build the model, Mean Absolute Error (MAE), Kappa Statistic and ROC Area. The obtained classification accuracies of different classifiers are in the range 88%-93%, 85%-91% and 83%-90% using 3-fold, 10-fold and hold-out (60%-40%) methods respectively. The results are very promising, indicating selected features were robust for diagnosis of diffuse goiter and also demonstrating the utility of machine learning techniques in medical domain.


Keywords


Machine Learning, Image Processing, Diffuse Goiter, Pre-Processing, Feature Extraction, Classification.

References