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All Journal International Journal of Electrical and Computer Engineering Journal of the Medical Sciences (Berkala Ilmu Kedokteran) Bulletin of Electrical Engineering and Informatics Medical Journal of Indonesia Jurnal Riset Kesehatan Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) Journal Of Vocational Health Studies Jurnal Kesehatan Vokasional Communications in Science and Technology Biosaintifika: Journal of Biology & Biology Education International Journal of Applied Sciences and Smart Technologies Indonesian Journal of Cancer JHeS (Journal of Health Studies) Indonesian Journal of Biomedicine and Clinical Sciences
Lina Choridah
Department Of Radiology, Faculty Of Medicine, And Public Health, And Nursing, Universitas Gadjah Mada/Dr. Sardjito General Hospital, Yogyakarta, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Medicine & Pharmacology Neuroscience Nursing Public Health

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Journal : Communications in Science and Technology

 1 
Texture feature extraction for the lung lesion density classification on computed tomography scan image Hasnely, Hasnely; Nugroho, Hanung Adi; Wibirama, Sunu; Windarta, Budi; Choridah, Lina
Communications in Science and Technology Vol 1 No 1 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.1.2016.14

Abstract

The radiology examination by computed tomography (CT) scan is an early detection of lung cancer to minimize the mortality rate. However, the assessment and diagnosis by an expert are subjective depending on the competence and experience of a radiologist. Hence, a digital image processing of CT scan is necessary as a tool to diagnose the lung cancer. This research proposes a morphological characteristics method for detecting lung cancer lesion density by using the histogram and GLCM (Gray Level Co-occurrence Matrices). The most well-known artificial neural network (ANN) architecture that is the multilayers perceptron (MLP), is used in classifying lung cancer lesion density of heterogeneous and homogeneous. Fifty CT scan images of lungs obtained from the Department of Radiology of RSUP Dr. Sardjito Hospital, Yogyakarta are used as the database. The results show that the proposed method achieved the accuracy of 98%, sensitivity of 96%, and specificity of 96%.
Internal content classification of ultrasound thyroid nodules based on textural features Nugroho, Anan; Nugroho, Hanung Adi; Setiawan, Noor Akhmad; Choridah, Lina
Communications in Science and Technology Vol 1 No 2 (2016)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.1.2.2016.25

Abstract

Ultrasound (US) is one of the best imaging modalities on thyroid identification. The suspicious thyroid is indicated in the existence of palpable nodules whose solid or cystic composition. Solid nodules have high possibility to be malignant than cystic. An effort to detect and classify the internal content of thyroid nodule has become challenge problem in radiology area. Operator dependence of ultrasound imaging makes it complicated due to missing interpretation among radiologists. Objective Computer Aided Diagnosis (CAD) was designed to solve it which works on texture analysis of histogram statistic, gray level co-occurrence matrice (GLCM) and gray level run length matrices (GLRLM). The fine-needle aspiration cytology (FNAC) is not needed because the textural pattern is significantly different between solid and cystic nodules.  Multi-layer perceptron (MLP) was adopted to do classification process for 72 US thyroid images yield an accuracy of 90.28%, the sensitivity of 87.80%, specificity of 93.55% and precision of 94.74%.
Co-Authors Abdi Alhaq Ahmad Hamim Sadewa Ajeng Viska Icanervilia Alberta Vania Handoko Amnesti, Merry Andrew Nobiantoro Gunawan Anita Ekowati Anwar, Sumadi Lukman Ardiyanto, Jeffry Arif Faisal Bambang Supriyadi Bening Rahimi Titisari Bestari Ariningrum Setyawati Brigitta Natasya Halim Catharina R., Celine Dewajani Purnomosari Dewi Susanto, Revina Dian Angraeni Didik Setyo Heriyanto Endang Mahati Evi Artsini Ferronika, Paranita Frinces, Freshilla Sonia Gilang Argya Dyaksa Gusti, Abdi Marang Hanung Adi Nugroho Harry Nugroho Eko Surniyantoro Hasnely, Hasnely Herianto . Hermina Sukmaningtyas Huwaida, Azizah Ignatius Riwanto, Ignatius Indrawati Khusnul Ain Kuntjoro, Lydia Purna Kusumasari, Dyan Pramandita Windu Kusumawardani, Aurisa Winda Mohammad Rizki Pratama Muqmiroh, Lailatul Naela Himayati Afifah Nastiti Rahajeng Noor Akhmad Setiawan Nugroho, Anan Nur Arfian Nurhuda Hendra Setyawan Pravitha, Clarissa Aulia Pribadi, Amri Wicaksono Rahajeng, Nastiti Rahman, Afif Rengganis, Anggraeni Ayu Riries Rulaningtyas Rita Cempaka Ryan Feraldy Haroen Serfina F., Eunike Setyawan, Nurhuda Hendra Shofwatul ‘Uyun Siti Masrochah Sofiati Purnami SOFIATI PURNAMI Sri Dwidanarti Suhartono Sumoro, William Sunu Wibirama Suprayitno Suprayitno Surniyantoro, Harry Nugroho Eko Teguh Aryandono Tjandra, Kevin Christian Torana Kurniawan Vincent Laiman Vincent Lau Viria Agesti Suvifan Viria Agesti Suvifan widyasari, dina Wigati Dhamiyati Windarta, Budi Yan Wisnu Prajoko Yana Supriatna Yanti Lusiyanti Yanti Lusiyanti Yosef Agung Cahyanta Yudistiarta, Devina Yuliana F., Ayu