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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Feature analysis for stage identification of Plasmodium vivax based on digital microscopic image Hanung Adi Nugroho; I Md. Dendi Maysanjaya; Noor Akhmad Setiawan; E. Elsa Herdiana Murhandarwati; Widhia K.Z Oktoeberza
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp721-728

Abstract

Plasmodium parasite is identified to confirm malaria disease.  Paramedics need to observe the presence of this parasite prepared on thick and thin blood films under microscope.  However, false identification still occurs which is caused by human factor during the examination.  Thus, malaria identification based on digital image processing has been widely developed to overcome the error possibility.  This paper proposes a scheme to identify and classify the stages of Plasmodium vivax parasite on digital microscopic image of thin blood films based on feature analysis.  Shape and texture features are extracted from segmented parasite objects.   Feature selection based on wrapper method is then conducted to obtain relevant features which may contribute in improving the classification result.  The classification process is conducted based on Naïve Bayes classifier.  The performance of proposed method is evaluated using 73 digital microscopic images of P.vivax parasite on thin blood films comprising of 29 trophozoites, 10 schizonts and 34 gametocytes stages.  By using six selected features including perimeter, dispersion, mean of intensity, ASM, contrast GLCM and entropy GLCM, the proposed scheme achieves the best classification rate with the accuracy, sensitivity and specificity of 97.29%, 97.30% and 97.30%, respectively.  This indicates that the proposed scheme has a potential to be implemented in the development of a computerised aided malaria diagnosis system for assisting the paramedics.
Comparison of Multiscale Entropy for Lung Sound Classification Achmad Rizal; Risanuri Hidayat; Hanung Adi Nugroho
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp984-994

Abstract

Lung sound is a biological signal that can be used to determine the health level of the respiratory tract. Various digital signal processing techniques have been developed for automatic classification of lung sounds. Entropy is one of the parameters used to measure the biomedical signal complexity. Multiscale entropy is introduced to measure the entropy of a signal at a particular scale range. Over time, various multiscale entropy techniques have been proposed to measure the complexity of biological signals and other physical signals. In this paper, some multiscale entropy techniques for lung sound classification are compared. The result of the comparison indicates that the Multiscale Permutation Entropy (MPE) produces the highest accuracy of 97.98% for five lung sound datasets. The result was achieved for the scale 1-10 producing ten features for each lung sound data. This result is better than other seven entropies. Multiscale entropy analysis can improve the accuracy of lung sound classification without requiring any features other than entropy.
Shape analysis for classification of breast nodules on digital ultrasound images Hanung Adi Nugroho; Hesti Khuzaimah Nurul Yusufiyah; Teguh Bharata Adji; Widhia K.Z Oktoeberza
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp837-844

Abstract

One of the imaging modalities for early detection of breast cancer malignancy is ultrasonography (USG).  The malignancy can be analysed from the characteristic of nodule shape.  This study aims to develop a method for classifying the shape of breast nodule into two classes, namely regular and irregular classes.  The input image is pre-processed by using the combination of adaptive median filter and speckle reduction bilateral filtering (SRBF) to reduce speckle noises and to eliminate the image label.  Afterwards, the filtered image is segmented based on active contour followed by feature extraction process.  Nine extracted features, i.e. roundness, slimness and seven features of invariant moments, are used to classify nodule shape using multi-layer perceptron (MLP).  The performance of the proposed method is evaluated using 105 breast nodule images which comprise of 57 regular and 48 irregular nodule images.  The results of classification process achieve the level of accuracy, sensitivity and specificity at 96.20%, 97.90% and 94.70%, respectively.  These results indicate that the proposed method successfully classifies the breast nodule images based on shape analysis.
Identification of plasmodium falciparum and plasmodium vivax on digital image of thin blood films gf Hanung Adi Nugroho; Made Satria Wibawa; Noor Akhmad Setiawan; E. Elsa Herdiana Murhandarwati; Ratna Lestari Budiani Buana
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.pp933-944

Abstract

Observing presence of Plasmodium parasite of stained thick or thin blood films through microscopic examination is a gold standard for malaria diagnosis.  Although the microscopic examination has been extensively used, misidentification might occur caused by human factors.  In order to overcome misidentification problem, several studies have been conducted to develop a computer-aided malaria diagnosis (CADx) to assist paramedics in decision-making.  This study proposes an approach to identify species and stage of Plasmodium falciparum and Plasmodium vivax on thin blood films collected from the Laboratory of Parasitology, Faculty of Medicine, Universitas Gadjah Mada.  Adaptive k-means clustering is applied to segment Plasmodium parasites.  A total of 39 features consisting of shape and texture features are extracted and then selected by using wrapper-based forward and backward directions.  Classification is evaluated in two schemes.  The first scheme is to classify the species of parasite into two classes. The second scheme is to classify the species and stage of parasite into six classes.  Three classifiers applied are k-nearest neighbour (KNN), support vector machine (SVM) and multi-layer perceptron (MLP).  Furthermore, to facilitate the multiclass classification, one-versus-one (OVO) and one-versus-all (OVA) methods are implemented.  The first scheme achieves the accuracy of 88.70% based on MLP classifier using three selected features.  While the accuracy gained by the second scheme is 95.16% based on OVO and MLP classifier using 29 selected features.  These results indicate that the proposed approach successfully identifies the species and stage of parasite on thin blood films and has potential to be implemented in the CADx system for assisting paramedics in diagnosing malaria.
Co-Authors - Nurfadilah, - A.A. Ketut Agung Cahyawan W Achmad Rizal Ade Sofa Adhistya Erna Permanasari Agus Eko Minarno Ahmad Nasikun Al-Fahsi, Resha Dwika Hefni Albert Ch. Soewongsono, Albert Ch. Alfarisi, Ikhsan Alfarozi, Syukron Abu Ishaq Anondho Wijanarko Aqil Aqthobirrobbany Aqthobirrobbany, Aqil Aras, Rezty Amalia Arham, Aulia Arif Masthori Atmaja Perdana, Chandra Ramadhan Azof Ghazali Sujono Bhisma Murti Cahyani Windarto Chitra Octavina Cindy Claudia Febiola, Cindy Claudia Citra Prasetyawati Cokro Mandiri, Mochammad Hazmi Danny Kurnianto Dewanta, Wika Dewi Kartika Sari Dian Nova Kusuma Hardani Dianursanti Dimas, Dimas Dindin Hidayat Dwi Haryono E. Elsa Herdiana Murhandarwati Elisabeth Deta Lustiyati Erwin Setyo Nugroho Eva Yuliana Fitri Faisal Najamuddin Fathania Firwan Firdaus Faza Maula Azif Fitri Bimantoro Ganesha L Putra Guyub Nuryanto Handani, Deni Hasdani, Hasdani Hasnely, Hasnely Hastuti, Uki Retno Budi Heri Hermansyah Heru Supriyono Hesti Khuzaimah Nurul Yusufiyah Hotama, Christianus Frederick Hutami, Augustine Herini Tita I Md. Dendi Maysanjaya Ibnu Taufan, Ibnu Ibrahim, Zaidah Ichsan Setiawan Igi Ardiyanto Ignatia Dhian Estu Karisma Ratri Imelda Imelda Indah Soesanti Indriana Hidayah Ismail Setiawan Jafaruddin Jafaruddin, Jafaruddin Kartika Firdausy Kirana, Thea Koko Ondara Krisna Nuresa Qodri KZ Widhia Oktoeberza Lina Choridah Listyalina, Latifah M. Khairun Iffat Made Satria Wibawa Maemonah, Maemonah Mahdi Abdullah Syihab Marshell Tendean Momoji Kubo Muhammad Bayu Sasongko Muhammad Rausan Fikri Naomi Shibasaki-Kitakawa Nasikun, Ahmad Ndii, Meksianis Z Nenden Siti Aminah Noor Abdul Haris Noor Akhmad Setiawan Nora Anisa Br. Sinulingga Novianti Puspitasari Nugroho, Anan Nur Fadhilah Nurcahyani Wulandari Nurfauzi, Rizki Oktoeberza, Widhia KZ Oyas Wahyunggoro Perdana, Adli Waliul Persada, Anugerah Galang Pranowo, Vicko Prasojo, Sasmito Praswasti P. D.K Wulan Puspitasari, Novianti Putri Bungsu Rachman, Anung Ratna Lestari Budiani Buana Rima Fitria Adiati Rina Sri Widayati Riri Ferdiana Risanuri Hidayat Rita Arbianti Rizky Naufal Perdana Robert Silas Kabanga Rochim, Febry Putra Roekmijati W. Soemantojo Saftirta Gatra Dewantara Sandy Anwar Mursito Sarjana Sarjana Sasongko Yoni Bagas Septian Rico Hernawan Setiyo Kantomo, Ilham Sudaryanto . Sukiyo Sukiyo Sumadi, Fauzi Dwi Setiawan Sunu Wibirama Suzanna Ndraha Syahrul Purnawan Syahwami, Syahwami Tania Surya Utami TATI NURHAYATI Teguh Bharata Adji Toshiy Yonemoto Tri Lestari Ulung Jantama Widhia K.Z Oktoeberza Widhia K.Z Oktoeberza Widya Sari Wika Dewanta Willy Anugrah Cahyadi Windarta, Budi Woraratpanya, Kuntpong Yenny Rahmawati Yuda Munarko Yufis Azhar Yulaikha Istiqomah Yulyanti, Vesi Zaidah Ibrahim Zubri, Aldino