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All Journal International Journal of Electrical and Computer Engineering Jurnal Media Infotama International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Communications in Science and Technology Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Informatika IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Hanung Adi Nugroho
Department Of Electrical And Information Engineering, Universitas Gadjah Mada
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : International Journal of Advances in Intelligent Informatics

 1 
A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform Kartika Firdausy; Oyas Wahyunggoro; Hanung Adi Nugroho; Muhammad Bayu Sasongko
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.914

Abstract

The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques
Systematic literature review of dermoscopic pigmented skin lesions classification using convolutional neural network (CNN) Erwin Setyo Nugroho; Igi Ardiyanto; Hanung Adi Nugroho
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.961

Abstract

The occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance.
Enhanced U-Net architecture with CNN backbone for accurate segmentation of skin lesions in dermoscopic images Aqthobirrobbany, Aqil; Al-Fahsi, Resha Dwika Hefni; Soesanti, Indah; Nugroho, Hanung Adi
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.1379

Abstract

Addressing the critical public health challenge of skin cancer, particularly melanoma and non-melanoma, this study focuses on enhancing early diagnosis through improved automatic segmentation of skin lesions in dermoscopic images. The researchers propose an optimized U-Net architecture that integrates advanced convolutional neural networks (CNNs) with backbone models such as ResNet50, VGG16, and MobileNetV2, specifically designed to handle the inherent variability and artifacts in dermoscopic imagery. The method's effectiveness was validated using the ISIC-2018 dataset, and our U-Net model incorporating the VGG16 backbone achieved notable improvements in segmentation accuracy, demonstrating an accuracy rate of 0.93. These results signify significant enhancements over existing methods, emphasizing the potential of the proposed approach in aiding precise skin cancer diagnosis and detection. This study makes a valuable contribution to dermatological imaging by presenting an advanced method that substantially boosts the accuracy of skin lesion segmentation, addressing a crucial need in public health.
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