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All Journal International Journal of Advances in Intelligent Informatics CESS (Journal of Computer Engineering, System and Science) Jiko (Jurnal Informatika dan komputer) Jurnal Teknologi Terpadu
Simeon Yuda Prasetyo
Bina Nusantara University
Computer Science & IT Engineering

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SARS-CoV-2 Detection from Lung CT-Scan Images Using Fine Tuning Concept on Deep-CNN Pretrained Model Simeon Yuda Prasetyo
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 1 (2023): January 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i1.40897

Abstract

The problem of the spread of COVID-19 (SARS-CoV-2) is spreading fleetly and worldwide. Beforehand discovery and opinion of complaint is veritably important to ensure the right remedy so that it needs to be enforced through various practical approaches. In former studies, complaint discovery through medical imaging has started to appear and get a good delicacy of around 80 to 90 percent using machine learning. In the deep learning era, some trials get better accuracy of 95 percent using the traditional deep learning approach. Now, deep learning has developed more fleetly, especially for image classification. therefore, it's necessary to experiment with a pretrained model approach to medical images. In addition, the fine tuning approach will also be an aspect of the approach that will be carried out in this trial to be compared and to find out its effect, specifically on CT-Scan images of the lungs for the discovery of COVID 19. The results of this experiment showed that the pretrained model approach can get high accuracy. Relatively high accuracy, the smallest testing accuracy in this trial reached 94.78 percent of the Xception without fine tuning phase, this result has beaten the machine learning approach which is didn't reach 90 percent of accuracy. The best experiment testing accuracy get 97.59 percet on the VGG 16 by applying fine tuning. The results of this trial also show that the fine tuning stage (for the top 10th layers) can increase the accuracy of the model.
BRAIN TUMOR DETECTION FROM MRI IMAGES USING DISCRETE COSINE TRANSFORM FEATURES AND EXTREME LEARNING MACHINE Simeon Yuda Prasetyo
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 1 (2023)
Publisher : JIKO (Jurnal Informatika dan Komputer)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i1.5230

Abstract

A brain tumor is an abnormal growth of brain tissue and characterized by excessive cell proliferation in certain parts of the brain. One of the current, reliable technologies that can be used to identify brain tumors is Magnetic Resonance Imaging (MRI) scans. The scanned MRI images are then conventionally monitored and examined by a specialist for the presence of tumors. As the number of people suffering from brain tumors is significantly increasing and their corresponding mortality rate has reached 18,600 by 2021, research on designing more effective and efficient tools to assist medical specialists in identifying brain tumors is considered of great importance. In a previous study, a machine learning-based model demonstrated its ability to detect brain tumors with a classification accuracy of 92%. Several hyperparameters were computationally tested using public MRI datasets to obtain the most reliable detection/binary classification accuracy on MRI brain images. Sophisticated model accuracy was achieved by testing various neuronal units and ELM activation functions, followed by inserting a feature map extracted from the Discrete Cosine Transform (DCT). The model obtained the highest testing accuracy of 95% with several 20 ELM neuron units with a tanh activation function.
Perbandingan Model Machine Learning pada Klasifikasi Tumor Otak Menggunakan Fitur Discrete Cosine Transform Simeon Yuda Prasetyo; Ghinaa Zain Nabiilah
Jurnal Teknologi Terpadu Vol. 9 No. 1 (2023): Juli, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i1.605

Abstract

Brain tumors are abnormal tissue growths characterized by excessive cell growth in certain brain parts. One of the reliable techniques currently available to identify brain tumors is using Magnetic Resonance Imaging (MRI) scans. The scanned MRI images are monitored and examined for tumor detection by a specialist. Developing more effective and efficient tools to help medical professionals identify brain tumors is urgent as the number of people suffering from brain tumors soars, and the death rate will reach 18,600 in 2021. In previous research, machine learning-based models demonstrated the ability to detect brain tumors with a classification accuracy of 92%, and this result is reliable. We computationally tested several hyperparameters using publicly available MRI datasets to obtain the most reliable binary classification accuracy in MRI brain images. A high level of model accuracy is achieved by testing various existing machine-learning model architectures and inserting a feature map extracted from the Discrete Cosine Transform (DCT). Classification of MRI images achieved the highest accuracy on test data at 93% using the Support Vector Machine (SVM) model.
Pneumonia Detection on X-Ray Imaging using Softmax Output in Multilevel Meta Ensemble Algorithm of Deep Convolutional Neural Network Transfer Learning Models Simeon Yuda Prasetyo; Ghinaa Zain Nabiilah; Zahra Nabila Izdihar; Sani Muhamad Isa
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.884

Abstract

Pneumonia is the leading cause of death from a single infection worldwide in children. A proven clinical method for diagnosing pneumonia is through a chest X-ray. However, the resulting X-ray images often need clarification, resulting in subjective judgments. In addition, the process of diagnosis requires a longer time. One technique can be applied by applying advanced deep learning, namely, Transfer Learning with Deep Convolutional Neural Network (Deep CNN) and modified Multilevel Meta Ensemble Learning using Softmax. The purpose of this research was to improve the accuracy of the pneumonia classification model. This study proposes a classification model with a meta-ensemble approach using five classification algorithms: Xception, Resnet 15V2, InceptionV3, VGG16, and VGG19. The ensemble stage used two different concepts, where the first level ensemble combined the output of the Xception, ResNet15V2, and InceptionV3 algorithms. Then the output from the first ensemble level is reused for the following learning process, combined with the output from other algorithms, namely VGG16 and VGG19. This process is called ensemble level two. The classification algorithm used at this stage is the same as the previous stage, using KNN as a classification model. Based on experiments, the model proposed in this study has better accuracy than the others, with a test accuracy value of 98.272%. The benefit of this research could help doctors as a recommendation tool to make more accurate and timely diagnoses, thus speeding up the treatment process and reducing the risk of complications.
Co-Authors Ghinaa Zain Nabiilah Sani Muhamad Isa Zahra Nabila Izdihar