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Lumini A, Roberto GF, Neves LA, Martins AS, do Nascimento MZ. Percolation Images: Fractal Geometry Features for Brain Tumor Classification. ADVANCES IN NEUROBIOLOGY 2024; 36:557-570. [PMID: 38468053 DOI: 10.1007/978-3-031-47606-8_29] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Brain tumor detection is crucial for clinical diagnosis and efficient therapy. In this work, we propose a hybrid approach for brain tumor classification based on both fractal geometry features and deep learning. In our proposed framework, we adopt the concept of fractal geometry to generate a "percolation" image with the aim of highlighting important spatial properties in brain images. Then both the original and the percolation images are provided as input to a convolutional neural network to detect the tumor. Extensive experiments, carried out on a well-known benchmark dataset, indicate that using percolation images can help the system perform better.
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Affiliation(s)
- Alessandra Lumini
- Department of Computer Science and Engineering, University of Bologna, Cesena, FC, Italy.
| | - Guilherme Freire Roberto
- Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP), São Carlos, SP, Brazil
| | - Leandro Alves Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), São José do Rio Preto, SP, Brazil
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Kumar A, Pandey SK, Varshney N, Singh KU, Singh T, Shah MA. Distinctive approach in brain tumor detection and feature extraction using biologically inspired DWT method and SVM. Sci Rep 2023; 13:22735. [PMID: 38123666 PMCID: PMC10733354 DOI: 10.1038/s41598-023-50073-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Brain tumors result from uncontrolled cell growth, potentially leading to fatal consequences if left untreated. While significant efforts have been made with some promising results, the segmentation and classification of brain tumors remain challenging due to their diverse locations, shapes, and sizes. In this study, we employ a combination of Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) to enhance performance and streamline the medical image segmentation process. Proposed method using Otsu's segmentation method followed by PCA to identify the most informative features. Leveraging the grey-level co-occurrence matrix, we extract numerous valuable texture features. Subsequently, we apply a Support Vector Machine (SVM) with various kernels for classification. We evaluate the proposed method's performance using metrics such as accuracy, sensitivity, specificity, and the Dice Similarity Index coefficient. The experimental results validate the effectiveness of our approach, with recall rates of 86.9%, precision of 95.2%, F-measure of 90.9%, and overall accuracy. Simulation of the results shows improvements in both quality and accuracy compared to existing techniques. In results section, experimental Dice Similarity Index coefficient of 0.82 indicates a strong overlap between the machine-extracted tumor region and the manually delineated tumor region.
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Affiliation(s)
- Ankit Kumar
- Department of Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India
| | - Saroj Kumar Pandey
- Department of Computer Engineering & Applications, GLA University, Mathura, Uttar Pradesh, India
| | - Neeraj Varshney
- Department of Computer Engineering & Applications, GLA University, Mathura, Uttar Pradesh, India
| | - Kamred Udham Singh
- School of Computer Science and Engineering, Graphic Hill Era University, Dehradun, 248002, India
| | - Teekam Singh
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, India
| | - Mohd Asif Shah
- Kebri Dehar University, Kebri Dehar, Somali, 250, Ethiopia.
- Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
- Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India.
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Yousaf F, Iqbal S, Fatima N, Kousar T, Shafry Mohd Rahim M. Multi-class disease detection using deep learning and human brain medical imaging. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Zhao T, Sun Z, Guo Y, Sun Y, Zhang Y, Wang X. Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms. Front Oncol 2023; 13:1169922. [PMID: 37274226 PMCID: PMC10233136 DOI: 10.3389/fonc.2023.1169922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023] Open
Abstract
Purpose To automatically evaluate renal masses in CT images by using a cascade 3D U-Net- and ResNet-based method to accurately segment and classify focal renal lesions. Material and Methods We used an institutional dataset comprising 610 CT image series from 490 patients from August 2009 to August 2021 to train and evaluate the proposed method. We first determined the boundaries of the kidneys on the CT images utilizing a 3D U-Net-based method to be used as a region of interest to search for renal mass. An ensemble learning model based on 3D U-Net was then used to detect and segment the masses, followed by a ResNet algorithm for classification. Our algorithm was evaluated with an external validation dataset and kidney tumor segmentation (KiTS21) challenge dataset. Results The algorithm achieved a Dice similarity coefficient (DSC) of 0.99 for bilateral kidney boundary segmentation in the test set. The average DSC for renal mass delineation using the 3D U-Net was 0.75 and 0.83. Our method detected renal masses with recalls of 84.54% and 75.90%. The classification accuracy in the test set was 86.05% for masses (<5 mm) and 91.97% for masses (≥5 mm). Conclusion We developed a deep learning-based method for fully automated segmentation and classification of renal masses in CT images. Testing of this algorithm showed that it has the capability of accurately localizing and classifying renal masses.
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Affiliation(s)
- Tongtong Zhao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Zhaonan Sun
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Ying Guo
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yumeng Sun
- Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Yaofeng Zhang
- Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
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Chmiel W, Kwiecień J, Motyka K. Saliency Map and Deep Learning in Binary Classification of Brain Tumours. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094543. [PMID: 37177747 PMCID: PMC10181656 DOI: 10.3390/s23094543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/24/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
The paper was devoted to the application of saliency analysis methods in the performance analysis of deep neural networks used for the binary classification of brain tumours. We have presented the basic issues related to deep learning techniques. A significant challenge in using deep learning methods is the ability to explain the decision-making process of the network. To ensure accurate results, the deep network being used must undergo extensive training to produce high-quality predictions. There are various network architectures that differ in their properties and number of parameters. Consequently, an intriguing question is how these different networks arrive at similar or distinct decisions based on the same set of prerequisites. Therefore, three widely used deep convolutional networks have been discussed, such as VGG16, ResNet50 and EfficientNetB7, which were used as backbone models. We have customized the output layer of these pre-trained models with a softmax layer. In addition, an additional network has been described that was used to assess the saliency areas obtained. For each of the above networks, many tests have been performed using key metrics, including statistical evaluation of the impact of class activation mapping (CAM) and gradient-weighted class activation mapping (Grad-CAM) on network performance on a publicly available dataset of brain tumour X-ray images.
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Affiliation(s)
- Wojciech Chmiel
- Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Al. Mickiewicza 30, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Joanna Kwiecień
- Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Al. Mickiewicza 30, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Kacper Motyka
- Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Al. Mickiewicza 30, AGH University of Science and Technology, 30-059 Krakow, Poland
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Mostafa AM, Zakariah M, Aldakheel EA. Brain Tumor Segmentation Using Deep Learning on MRI Images. Diagnostics (Basel) 2023; 13:diagnostics13091562. [PMID: 37174953 PMCID: PMC10177460 DOI: 10.3390/diagnostics13091562] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model's output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%.
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Affiliation(s)
- Almetwally M Mostafa
- Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
| | - Mohammed Zakariah
- Department of Computer Science, College of Computer and Information Science, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
| | - Eman Abdullah Aldakheel
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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ZainEldin H, Gamel SA, El-Kenawy ESM, Alharbi AH, Khafaga DS, Ibrahim A, Talaat FM. Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010018. [PMID: 36671591 PMCID: PMC9854739 DOI: 10.3390/bioengineering10010018] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/12/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Diagnosing a brain tumor takes a long time and relies heavily on the radiologist's abilities and experience. The amount of data that must be handled has increased dramatically as the number of patients has increased, making old procedures both costly and ineffective. Many researchers investigated a variety of algorithms for detecting and classifying brain tumors that were both accurate and fast. Deep Learning (DL) approaches have recently been popular in developing automated systems capable of accurately diagnosing or segmenting brain tumors in less time. DL enables a pre-trained Convolutional Neural Network (CNN) model for medical images, specifically for classifying brain cancers. The proposed Brain Tumor Classification Model based on CNN (BCM-CNN) is a CNN hyperparameters optimization using an adaptive dynamic sine-cosine fitness grey wolf optimizer (ADSCFGWO) algorithm. There is an optimization of hyperparameters followed by a training model built with Inception-ResnetV2. The model employs commonly used pre-trained models (Inception-ResnetV2) to improve brain tumor diagnosis, and its output is a binary 0 or 1 (0: Normal, 1: Tumor). There are primarily two types of hyperparameters: (i) hyperparameters that determine the underlying network structure; (ii) a hyperparameter that is responsible for training the network. The ADSCFGWO algorithm draws from both the sine cosine and grey wolf algorithms in an adaptable framework that uses both algorithms' strengths. The experimental results show that the BCM-CNN as a classifier achieved the best results due to the enhancement of the CNN's performance by the CNN optimization's hyperparameters. The BCM-CNN has achieved 99.98% accuracy with the BRaTS 2021 Task 1 dataset.
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Affiliation(s)
- Hanaa ZainEldin
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Samah A. Gamel
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - El-Sayed M. El-Kenawy
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
- Correspondence: (E.-S.M.E.-K.); (D.S.K.); (A.I.)
| | - Amal H. Alharbi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (E.-S.M.E.-K.); (D.S.K.); (A.I.)
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
- Correspondence: (E.-S.M.E.-K.); (D.S.K.); (A.I.)
| | - Fatma M. Talaat
- Machine Learning & Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33511, Egypt
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Abbas A, Gaber MM, Abdelsamea MM. XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:9875. [PMID: 36560243 PMCID: PMC9782528 DOI: 10.3390/s22249875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/06/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Of the various tumour types, colorectal cancer and brain tumours are still considered among the most serious and deadly diseases in the world. Therefore, many researchers are interested in improving the accuracy and reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised learning has been proven to be an effective solution when dealing with datasets with insufficient data annotations. However, medical image datasets often suffer from data irregularities, making the recognition task even more challenging. The class decomposition approach has provided a robust solution to such a challenging problem by simplifying the learning of class boundaries of a dataset. In this paper, we propose a robust self-supervised model, called XDecompo, to improve the transferability of features from the pretext task to the downstream task. XDecompo has been designed based on an affinity propagation-based class decomposition to effectively encourage learning of the class boundaries in the downstream task. XDecompo has an explainable component to highlight important pixels that contribute to classification and explain the effect of class decomposition on improving the speciality of extracted features. We also explore the generalisability of XDecompo in handling different medical datasets, such as histopathology for colorectal cancer and brain tumour images. The quantitative results demonstrate the robustness of XDecompo with high accuracy of 96.16% and 94.30% for CRC and brain tumour images, respectively. XDecompo has demonstrated its generalization capability and achieved high classification accuracy (both quantitatively and qualitatively) in different medical image datasets, compared with other models. Moreover, a post hoc explainable method has been used to validate the feature transferability, demonstrating highly accurate feature representations.
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Affiliation(s)
- Asmaa Abbas
- School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7AP, UK
| | - Mohamed Medhat Gaber
- School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7AP, UK
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
| | - Mohammed M. Abdelsamea
- School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7AP, UK
- Department of Computer Science, Faculty of Computers and Information, University of Assiut, Assiut 71515, Egypt
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Yu Z, Wang K, Wan Z, Xie S, Lv Z. Popular deep learning algorithms for disease prediction: a review. CLUSTER COMPUTING 2022; 26:1231-1251. [PMID: 36120180 PMCID: PMC9469816 DOI: 10.1007/s10586-022-03707-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 07/07/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field-integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.
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Affiliation(s)
- Zengchen Yu
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071 China
| | - Ke Wang
- Psychiatric Department, Qingdao Municipal Hospital, Zhuhai Road, Qingdao, 266071 China
| | - Zhibo Wan
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071 China
| | - Shuxuan Xie
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071 China
| | - Zhihan Lv
- Department of Game Design, Faculty of Arts, Uppsala University, 75105 Uppsala, Sweden
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An attention-guided convolutional neural network for automated classification of brain tumor from MRI. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07742-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Demir F, Akbulut Y. A new deep technique using R-CNN model and L1NSR feature selection for brain MRI classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Kursad Poyraz A, Dogan S, Akbal E, Tuncer T. Automated brain disease classification using exemplar deep features. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103448] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Biratu ES, Schwenker F, Ayano YM, Debelee TG. A Survey of Brain Tumor Segmentation and Classification Algorithms. J Imaging 2021; 7:jimaging7090179. [PMID: 34564105 PMCID: PMC8465364 DOI: 10.3390/jimaging7090179] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/25/2021] [Accepted: 08/28/2021] [Indexed: 01/16/2023] Open
Abstract
A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models' performance evaluation metrics.
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Affiliation(s)
- Erena Siyoum Biratu
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia; (E.S.B.); (T.G.D.)
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, Ulm University, 89081 Ulm, Germany
- Correspondence:
| | | | - Taye Girma Debelee
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia; (E.S.B.); (T.G.D.)
- Ethiopian Artificial Intelligence Center, Addis Ababa 40782, Ethiopia;
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