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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Nik Adilah Hanin Zahri
Universiti Malaysia Perlis

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Cervical cancer detection method using an improved cellular neural network (CNN) algorithm Azian Azamimi Abdullah; Aafion Fonetta Dickson Giong; Nik Adilah Hanin Zahri
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp210-218

Abstract

Cervical cancer is the second most common in Malaysia and the fourth frequent cancer among women in worldwide.  Pap smear test is often ignored although it is actually useful, beneficial and essential as screening tool for cervical cancer. However, Pap smear images have low sensitivity as well as specificity. Therefore, it is difficult to determine whether the abnormal cells are cancerous or not. Recently, computer-based algorithms are widely used in cervical cancer screening. In this study, an improved cellular neural network (CNN) algorithm is proposed as the solution to detect the cancerous cells in real-time by undergoing the image processing of Pap smear images. A few templates are combined and modified to form an ideal CNN algorithm to detect the cancerous cells in total of 115 Pap smear images. A MATLAB based CNN is developed for an automated detection of cervix cancerous cells where the templates segmented the nucleus of the cells. From the simulation results, our proposed CNN algorithm can detect the cervix cancer cells automatically with more than 88% accuracy.
Snake species identification by using natural language processing Nur Liyana Izzati Rusli; Amiza Amir; Nik Adilah Hanin Zahri; R. Badlishah Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp999-1006

Abstract

The paper presents the snake species identification by using natural language processing. It aims to help medical professionals in predicting the snake species for snake-bite treatments based on the patient’s description of the snake. The decision in suitable anti-venom critically depends on the type of snake species. Wrong anti-venom may result in severe morbidity and mortality. This research investigates the human perception and the selection of words in describing a snake based on their visual view. The descriptions were presented in unstructured text, and the NLP processing involves pre-processing, feature extraction and classification. Four machine learning algorithms (naïve Bayes, k-Nearest Neighbour, Support Vector Machine, and Decision Trees J48) were used during training and classification. Our results show that J48 algorithm obtained the highest classification accuracy of 71.6% correct prediction for the NLP-Snake data set with high precision and recall.
Co-Authors Aafion Fonetta Dickson Giong Amiza Amir Azian Azamimi Abdullah Nur Liyana Izzati Rusli R. Badlishah Ahmad