0% Complete
Home
/
12th International Conference on Computer and Knowledge Engineering
Fatty Liver Level Recognition Using Particle Swarm Optimization (PSO) Image Segmentation and Analysis
Authors :
Seyed Muhammad Hossein Mousavi
1
Vyacheslav Lyashenko
2
Atiye Ilanloo
3
S. Younes Mirinezhad
4
1- Independent Scientist, Tehran, Iran
2- Kharkiv National University of Radio Electronics Media Systems and Technologies Department Kharkiv, Ukraine
3- Faculty of Humanities- Psychology, Islamic Azad University of Rasht, Gilan, Iran
4- Independent Scientist, Tehran, Iran
Keywords :
Fatty Liver Detection،Expert System،PSO،Image Segmentation،Fat Deposit،Hepatic Glycogen
Abstract :
Fatty liver or liver hepatic glycogen is one of the most common disorders of liver, nowadays. Clinical detection of this disorder by human expert is increasing as our lifestyle leads us toward this phenomenon. So, making a fast and robust expert system for fatty liver detection is essential in each clinic and that’s why we intended to make one. Proposed expert system, works based on variety of image processing techniques and algorithms to detect fatty liver and recognize its level by four markers. Four segmentation techniques of Otsu, Watershed, K-Means and Particle Swarm Optimization (PSO) are employed to determine disorder level. Performance metrics of Accuracy, F-Score and IoU or Jaccard evaluated the robustness of the proposed system. Finally, fatty liver level is calculated based on amount of fat deposits inside segmented image. Experiments are conducted on multiple data sample in high resolution with microscope zoom bigger or equal of 200 which are collected from the internet. All performance metrics and comparisons returned satisfactory results in comparing with traditional methods. Proposed system could achieve average accuracy value of 0.922 for all samples comparing with ground truth data. Additionally, F-Score and IoU performance metrics returned values are 0.872 and 0.907, respectively
Papers List
List of archived papers
Predicting cascading failure with machine learning methods in the interdependent networks
Mohamad Hossein Maghsoodi - Mohamad Khansari
An influence maximization algorithm based on community detection using topological features
Zahra Aghaee - Afsaneh Fatemi
Enhancing Cloud Security with Federated CNN-LSTM: A Novel Approach to Intrusion Detection
Reyhaneh Ilaghi - Raheleh Ilaghi - Fereshteh Rahmani - Seyyed hamid Ghafoori
Zone-Based Federated Learning in Indoor Positioning
Omid Tasbaz - Vahideh Moghtadaiee - Bahar Farahani
Energy Efficient Power Allocation in MIMO-NOMA Systems with ZF Receiver Beamforming in Multiple Clusters
Mahdi Nangir - Abdolrasoul Sakhaei Gharagezlou - Nima Imani
WBT-GAN:Wavelet based Generative Adversarial Network for Texture Synthesis
Sara Saberi moghadam - Reza Azmi - Maral Zarvani
Depression Diagnosis Using Optimization of Nonlinear EEG Features Based on Parametric Learning Tactics
Ali Asadi Zeidabadi - Melika Changizi - Mahdi Zolfagharzadeh Kermani - Sara Bargi Barkouk
Adversarial Robustness Evaluation with Separation Index
Bahareh Kaviani Baghbaderani - Afsaneh Hasanebrahimi - Ahmad Kalhor - Reshad Hosseini
Speech Emotion Recognition Using a Hierarchical Adaptive Weighted Multi-Layer Sparse Auto-Encoder Extreme Learning Machine with New Weighting and Spectral/SpectroTemporal Gabor Filter Bank Features
Fatemeh Daneshfar - Seyed Jahanshah Kabudian
Distinguishing Abstracts of Human-Written and ChatGPT-Generated Papers in the Field of Computer Science
Mohsen Arzani - Hamed Vahdat-Nejad - Matin Hossein-Pour
more
Samin Hamayesh - Version 41.7.6