1
|
Kaifi R. A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification. Diagnostics (Basel) 2023; 13:3007. [PMID: 37761373 PMCID: PMC10527911 DOI: 10.3390/diagnostics13183007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.
Collapse
Affiliation(s)
- Reham Kaifi
- Department of Radiological Sciences, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah City 22384, Saudi Arabia;
- King Abdullah International Medical Research Center, Jeddah City 22384, Saudi Arabia
- Medical Imaging Department, Ministry of the National Guard—Health Affairs, Jeddah City 11426, Saudi Arabia
| |
Collapse
|
2
|
Mahum R, Sharaf M, Hassan H, Liang L, Huang B. A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding. Biomedicines 2023; 11:1715. [PMID: 37371810 DOI: 10.3390/biomedicines11061715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/09/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
A brain tumor refers to an abnormal growth of cells in the brain that can be either benign or malignant. Oncologists typically use various methods such as blood or visual tests to detect brain tumors, but these approaches can be time-consuming, require additional human effort, and may not be effective in detecting small tumors. This work proposes an effective approach to brain tumor detection that combines segmentation and feature fusion. Segmentation is performed using the mayfly optimization algorithm with multilevel Kapur's threshold technique to locate brain tumors in MRI scans. Key features are achieved from tumors employing Histogram of Oriented Gradients (HOG) and ResNet-V2, and a bidirectional long short-term memory (BiLSTM) network is used to classify tumors into three categories: pituitary, glioma, and meningioma. The suggested methodology is trained and tested on two datasets, Figshare and Harvard, achieving high accuracy, precision, recall, F1 score, and area under the curve (AUC). The results of a comparative analysis with existing DL and ML methods demonstrate that the proposed approach offers superior outcomes. This approach has the potential to improve brain tumor detection, particularly for small tumors, but further validation and testing are needed before clinical use.
Collapse
Affiliation(s)
- Rabbia Mahum
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan
| | - Mohamed Sharaf
- Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
| | - Haseeb Hassan
- College of Big Data and Internet, Shenzhen Technology University (SZTU), Shenzhen 518118, China
| | - Lixin Liang
- College of Big Data and Internet, Shenzhen Technology University (SZTU), Shenzhen 518118, China
| | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University (SZTU), Shenzhen 518118, China
| |
Collapse
|
3
|
Nalepa J, Kotowski K, Machura B, Adamski S, Bozek O, Eksner B, Kokoszka B, Pekala T, Radom M, Strzelczak M, Zarudzki L, Krason A, Arcadu F, Tessier J. Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients. Comput Biol Med 2023; 154:106603. [PMID: 36738710 DOI: 10.1016/j.compbiomed.2023.106603] [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: 10/19/2022] [Revised: 01/11/2023] [Accepted: 01/22/2023] [Indexed: 02/05/2023]
Abstract
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.
Collapse
Affiliation(s)
- Jakub Nalepa
- Graylight Imaging, Gliwice, Poland; Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
| | | | | | | | - Oskar Bozek
- Department of Radiodiagnostics and Invasive Radiology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland
| | - Bartosz Eksner
- Department of Radiology and Nuclear Medicine, ZSM Chorzów, Chorzów, Poland
| | - Bartosz Kokoszka
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland
| | - Tomasz Pekala
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland
| | - Mateusz Radom
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Marek Strzelczak
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Lukasz Zarudzki
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Agata Krason
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
| | - Filippo Arcadu
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Informatics, Roche Innovation Center Basel, Basel, Switzerland
| | - Jean Tessier
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
| |
Collapse
|
4
|
Umer MJ, Sharif MI. A Comprehensive Survey on Quantum Machine Learning and Possible Applications. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2022. [DOI: 10.4018/ijehmc.315730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
Collapse
|
5
|
Popat M, Patel S. Research perspective and review towards brain tumour segmentation and classification using different image modalities. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2124546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Mayuri Popat
- U & P.U. Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology (CSPIT), Charotar University of Science and Technology (CHARUSAT), Gujarat, India
| | - Sanskruti Patel
- Smt. Chandaben Mohanbhai Patel Institute of Computer Applications (CMPICA), Charotar University of Science and Technology (CHARUSAT), Gujarat, India
| |
Collapse
|
6
|
An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD. TOMOGRAPHY (ANN ARBOR, MICH.) 2022; 8:1905-1927. [PMID: 35894026 PMCID: PMC9330870 DOI: 10.3390/tomography8040161] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 06/28/2022] [Accepted: 07/13/2022] [Indexed: 01/05/2023]
Abstract
A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis, as a minor human judgment can eventually cause severe consequences. Magnetic Resonance Image (MRI) serves as a non-invasive tool to detect the presence of a tumor. However, Rician noise is inevitably instilled during the image acquisition process, which leads to poor observation and interferes with the treatment. Computer-Aided Diagnosis (CAD) systems can perform early diagnosis of the disease, potentially increasing the chances of survival, and lessening the need for an expert to analyze the MRIs. Convolutional Neural Networks (CNN) have proven to be very effective in tumor detection in brain MRIs. There have been multiple studies dedicated to brain tumor classification; however, these techniques lack the evaluation of the impact of the Rician noise on state-of-the-art deep learning techniques and the consideration of the scaling impact on the performance of the deep learning as the size and location of tumors vary from image to image with irregular shape and boundaries. Moreover, transfer learning-based pre-trained models such as AlexNet and ResNet have been used for brain tumor detection. However, these architectures have many trainable parameters and hence have a high computational cost. This study proposes a two-fold solution: (a) Multi-Scale CNN (MSCNN) architecture to develop a robust classification model for brain tumor diagnosis, and (b) minimizing the impact of Rician noise on the performance of the MSCNN. The proposed model is a multi-class classification solution that classifies MRIs into glioma, meningioma, pituitary, and non-tumor. The core objective is to develop a robust model for enhancing the performance of the existing tumor detection systems in terms of accuracy and efficiency. Furthermore, MRIs are denoised using a Fuzzy Similarity-based Non-Local Means (FSNLM) filter to improve the classification results. Different evaluation metrics are employed, such as accuracy, precision, recall, specificity, and F1-score, to evaluate and compare the performance of the proposed multi-scale CNN and other state-of-the-art techniques, such as AlexNet and ResNet. In addition, trainable and non-trainable parameters of the proposed model and the existing techniques are also compared to evaluate the computational efficiency. The experimental results show that the proposed multi-scale CNN model outperforms AlexNet and ResNet in terms of accuracy and efficiency at a lower computational cost. Based on experimental results, it is found that our proposed MCNN2 achieved accuracy and F1-score of 91.2% and 91%, respectively, which is significantly higher than the existing AlexNet and ResNet techniques. Moreover, our findings suggest that the proposed model is more effective and efficient in facilitating clinical research and practice for MRI classification.
Collapse
|
7
|
Rajeswari R, Neelima G, Maram B, Angadi A. MVPO Predictor: Deep Learning-Based Tumor Classification and Survival Prediction of Brain Tumor Patients with MRI Using Multi-Verse Political Optimizer. INT J PATTERN RECOGN 2022. [DOI: 10.1142/s0218001422520061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Brain tumor is a severe nervous disorder that causes damage to health and often leads to death. Therefore, it is significant to classify the brain tumor at an early stage as it increases the survival rate of patients. One of the commonly employed imaging modalities for brain tumor classification is Magnetic Resonance Imaging (MRI). However, it is relatively complex to perform the brain tumor classification process due to the variations of type, shape, size and tumor location. To overcome such issues and classify the tumor more accurately, a deep learning classifier named Deep Maxout network is developed to classify the tumor into different grades. Based on the classification result, the features connected with the tumor grades are effectively acquired to make the survival prediction process. Deep learning is an effective and robust classifier model employed to perform the tumor classification or detection process with the MRI modality. Here, the survival prediction of tumor patients is carried out by the Deep Long Short-Term Memory (LSTM) classifier. Accordingly, the proposed method achieved higher performance using accuracy, sensitivity, specificity and prediction error with the values of 0.9434, 0.9324, 0.9202 and 0.0579.
Collapse
Affiliation(s)
- R. Rajeswari
- Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai 600124, Tamil Nadu, India
| | - G. Neelima
- Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Gajuwaka, Visakhapatnam 530049, Andhra Pradesh, India
| | - Balajee Maram
- Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Srikakulam 532127, Andhra Pradesh, India
| | - Anupama Angadi
- Department of Information Technology, Anil Neerukonda Institute of Technology & Sciences, Bheemunipatnam, Visakhapatnam 531162, Andhra Pradesh, India
| |
Collapse
|
8
|
Abstract
AbstractBrain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.
Collapse
|
9
|
Nawaz M, Nazir T, Masood M, Mehmood A, Mahum R, Khan MA, Kadry S, Thinnukool O. Analysis of Brain MRI Images Using Improved CornerNet Approach. Diagnostics (Basel) 2021; 11:diagnostics11101856. [PMID: 34679554 PMCID: PMC8535141 DOI: 10.3390/diagnostics11101856] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 01/18/2023] Open
Abstract
The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.
Collapse
Affiliation(s)
- Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | - Tahira Nazir
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | - Momina Masood
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | - Awais Mehmood
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | - Rabbia Mahum
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.N.); (T.N.); (M.M.); (A.M.); (R.M.)
| | | | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway;
| | - Orawit Thinnukool
- Research Group of Embedded Systems and Mobile Application in Health Science, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
- Correspondence:
| |
Collapse
|
10
|
Sethy PK, Behera SK. A data constrained approach for brain tumour detection using fused deep features and SVM. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:28745-28760. [DOI: 10.1007/s11042-021-11098-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 04/14/2021] [Accepted: 05/21/2021] [Indexed: 08/02/2023]
|
11
|
Sharif MI, Khan MA, Alhussein M, Aurangzeb K, Raza M. A decision support system for multimodal brain tumor classification using deep learning. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00321-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
AbstractMulticlass classification of brain tumors is an important area of research in the field of medical imaging. Since accuracy is crucial in the classification, a number of techniques are introduced by computer vision researchers; however, they still face the issue of low accuracy. In this article, a new automated deep learning method is proposed for the classification of multiclass brain tumors. To realize the proposed method, the Densenet201 Pre-Trained Deep Learning Model is fine-tuned and later trained using a deep transfer of imbalanced data learning. The features of the trained model are extracted from the average pool layer, which represents the very deep information of each type of tumor. However, the characteristics of this layer are not sufficient for a precise classification; therefore, two techniques for the selection of features are proposed. The first technique is Entropy–Kurtosis-based High Feature Values (EKbHFV) and the second technique is a modified genetic algorithm (MGA) based on metaheuristics. The selected features of the GA are further refined by the proposed new threshold function. Finally, both EKbHFV and MGA-based features are fused using a non-redundant serial-based approach and classified using a multiclass SVM cubic classifier. For the experimental process, two datasets, including BRATS2018 and BRATS2019, are used without increase and have achieved an accuracy of more than 95%. The precise comparison of the proposed method with other neural nets shows the significance of this work.
Collapse
|
12
|
Liaqat A, Khan MA, Sharif M, Mittal M, Saba T, Manic KS, Al Attar FNH. Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review. Curr Med Imaging 2021; 16:1229-1242. [PMID: 32334504 DOI: 10.2174/1573405616666200425220513] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 01/14/2020] [Accepted: 01/30/2020] [Indexed: 11/22/2022]
Abstract
Recent facts and figures published in various studies in the US show that approximately
27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that
the mortality rate is quite high in diagnosed cases. The early detection of these infections can save
precious human lives. As the manual process of these infections is time-consuming and expensive,
therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy
specialists in their clinics. Generally, an automated method of gastric infection detections
using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing,
feature extraction, segmentation of infected regions, and classification into their relevant
categories. These steps consist of various challenges that reduce the detection and recognition
accuracy as well as increase the computation time. In this review, authors have focused on the importance
of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and
the scope of endoscopy. Further, the general steps and highlighting the importance of each step
have been presented. A detailed discussion and future directions have been provided at the end.
Collapse
Affiliation(s)
- Amna Liaqat
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | | | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Mamta Mittal
- Department of Computer Science & Engineering, G.B. Pant Govt. Engineering College, New Delhi, India
| | - Tanzila Saba
- Department of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - K. Suresh Manic
- Department of Electrical & Computer Engineering, National University of Science & Technology, Muscat, Oman
| | | |
Collapse
|
13
|
Raghavendra U, Pham TH, Gudigar A, Vidhya V, Rao BN, Sabut S, Wei JKE, Ciaccio EJ, Acharya UR. Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-020-00257-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
AbstractBrain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment. Computer-aided diagnosis is a type of non-invasive diagnostic tool which can help in detecting life-threatening disease in its early stage by utilizing image processing and soft computing techniques. In this paper, we have developed one such model to assess intracerebral haemorrhage by employing non-linear features combined with a probabilistic neural network classifier and computed tomography (CT) images. Our model achieved a maximum accuracy of 97.37% in discerning normal versus haemorrhagic subjects. An intracerebral haemorrhage index is also developed using only three significant features. The clinical and statistical validation of the model confirms its suitability in providing for improved treatment planning and in making strategic decisions.
Collapse
|
14
|
Sadad T, Rehman A, Hussain A, Abbasi AA, Khan MQ. A Review on Multi-organ Cancer Detection Using Advanced Machine Learning Techniques. Curr Med Imaging 2020; 17:686-694. [PMID: 33334293 DOI: 10.2174/1573405616666201217112521] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/07/2020] [Accepted: 07/23/2020] [Indexed: 12/24/2022]
Abstract
Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lowers the mortality rate. However, this can be difficult due to various factors related to imaging modalities, such as complex background, low contrast, brightness issues, poorly defined borders and the shape of the affected area. Recently, computer-aided diagnosis (CAD) models have been used to accurately diagnose tumors in different parts of the human body, especially breast, brain, lung, liver, skin and colon cancers. These cancers are diagnosed using various modalities, including computed tomography (CT), magnetic resonance imaging (MRI), colonoscopy, mammography, dermoscopy and histopathology. The aim of this review was to investigate existing approaches for the diagnosis of breast, brain, lung, liver, skin and colon tumors. The review focuses on decision-making systems, including handcrafted features and deep learning architectures for tumor detection.
Collapse
Affiliation(s)
- Tariq Sadad
- Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan
| | - Amjad Rehman
- Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Ayyaz Hussain
- Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan
| | - Aaqif Afzaal Abbasi
- Department of Software Engineering, Foundation University, Islamabad, Pakistan
| | - Muhammad Qasim Khan
- Department of Computer Science, COMSATS University (Attock Campus) Islamabad, Pakistan
| |
Collapse
|
15
|
Lu SY, Wang SH, Zhang YD. A classification method for brain MRI via MobileNet and feedforward network with random weights. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.10.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
16
|
Khan MA, Ashraf I, Alhaisoni M, Damaševičius R, Scherer R, Rehman A, Bukhari SAC. Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists. Diagnostics (Basel) 2020; 10:565. [PMID: 32781795 PMCID: PMC7459797 DOI: 10.3390/diagnostics10080565] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/01/2020] [Accepted: 08/04/2020] [Indexed: 11/17/2022] Open
Abstract
Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.
Collapse
Affiliation(s)
- Muhammad Attique Khan
- Department of Computer Science, HITEC University, Museum Road, Taxila 47080, Pakistan;
| | - Imran Ashraf
- Department of Computer Engineering, HITEC University, Museum Road, Taxila 47080, Pakistan;
| | - Majed Alhaisoni
- College of Computer Science and Engineering, University of Ha’il, Ha’il 81451, Saudi Arabia;
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
| | - Rafal Scherer
- Department of Intelligent Computer Systems, Czestochowa University of Technology, 42-200 Czestochowa, Poland;
| | - Amjad Rehman
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
| | - Syed Ahmad Chan Bukhari
- Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John’s University, New York, NY 11439, USA;
| |
Collapse
|
17
|
Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10326-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
|
18
|
A Customized VGG19 Network with Concatenation of Deep and Handcrafted Features for Brain Tumor Detection. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10103429] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Brain tumor (BT) is one of the brain abnormalities which arises due to various reasons. The unrecognized and untreated BT will increase the morbidity and mortality rates. The clinical level assessment of BT is normally performed using the bio-imaging technique, and MRI-assisted brain screening is one of the universal techniques. The proposed work aims to develop a deep learning architecture (DLA) to support the automated detection of BT using two-dimensional MRI slices. This work proposes the following DLAs to detect the BT: (i) implementing the pre-trained DLAs, such as AlexNet, VGG16, VGG19, ResNet50 and ResNet101 with the deep-features-based SoftMax classifier; (ii) pre-trained DLAs with deep-features-based classification using decision tree (DT), k nearest neighbor (KNN), SVM-linear and SVM-RBF; and (iii) a customized VGG19 network with serially-fused deep-features and handcrafted-features to improve the BT detection accuracy. The experimental investigation was separately executed using Flair, T2 and T1C modality MRI slices, and a ten-fold cross validation was implemented to substantiate the performance of proposed DLA. The results of this work confirm that the VGG19 with SVM-RBF helped to attain better classification accuracy with Flair (>99%), T2 (>98%), T1C (>97%) and clinical images (>98%).
Collapse
|
19
|
|
20
|
Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA. Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04650-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
21
|
Khan MA, Sharif M, Akram T, Yasmin M, Nayak RS. Stomach Deformities Recognition Using Rank-Based Deep Features Selection. J Med Syst 2019; 43:329. [PMID: 31676931 DOI: 10.1007/s10916-019-1466-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Accepted: 09/26/2019] [Indexed: 12/22/2022]
Abstract
Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.
Collapse
Affiliation(s)
| | - Muhammad Sharif
- Department of E&CE, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan.
| | - Tallha Akram
- Information Science, Canara Engineering College, Mangaluru, Karnataka, India
| | - Mussarat Yasmin
- Department of E&CE, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan
| | - Ramesh Sunder Nayak
- Department of CS, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan
| |
Collapse
|
22
|
Khan MA, Rashid M, Sharif M, Javed K, Akram T. Classification of gastrointestinal diseases of stomach from WCE using improved saliency-based method and discriminant features selection. MULTIMEDIA TOOLS AND APPLICATIONS 2019; 78:27743-27770. [DOI: 10.1007/s11042-019-07875-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 04/22/2019] [Accepted: 06/10/2019] [Indexed: 08/25/2024]
|