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Dong J, Zhang G, Hu Y, Wu Y, Rong H. An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation. Int J Neural Syst 2024; 34:2450036. [PMID: 38686911 DOI: 10.1142/s0129065724500369] [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] [Indexed: 05/02/2024]
Abstract
Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved.
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Affiliation(s)
- Jianping Dong
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Gexiang Zhang
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yangheng Hu
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yijin Wu
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Haina Rong
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
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2
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Zhang Z, Zhang X, Yang Y, Liu J, Zheng C, Bai H, Ma Q. Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-TransUNet. Front Neurosci 2023; 17:1207149. [PMID: 37292160 PMCID: PMC10244508 DOI: 10.3389/fnins.2023.1207149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/09/2023] [Indexed: 06/10/2023] Open
Abstract
Acoustic neuroma is one of the most common tumors in the cerebellopontine angle area. Patients with acoustic neuroma have clinical manifestations of the cerebellopontine angle occupying syndrome, such as tinnitus, hearing impairment and even hearing loss. Acoustic neuromas often grow in the internal auditory canal. Neurosurgeons need to observe the lesion contour with the help of MRI images, which not only takes a lot of time, but also is easily affected by subjective factors. Therefore, the automatic and accurate segmentation of acoustic neuroma in cerebellopontine angle on MRI is of great significance for surgical treatment and expected rehabilitation. In this paper, an automatic segmentation method based on Transformer is proposed, using TransUNet as the core model. As some acoustic neuromas are irregular in shape and grow into the internal auditory canal, larger receptive fields are thus needed to synthesize the features. Therefore, we added Atrous Spatial Pyramid Pooling to CNN, which can obtain a larger receptive field without losing too much resolution. Since acoustic neuromas often occur in the cerebellopontine angle area with relatively fixed position, we combined channel attention with pixel attention in the up-sampling stage so as to make our model automatically learn different weights by adding the attention mechanism. In addition, we collected 300 MRI sequence nuclear resonance images of patients with acoustic neuromas in Tianjin Huanhu hospital for training and verification. The ablation experimental results show that the proposed method is reasonable and effective. The comparative experimental results show that the Dice and Hausdorff 95 metrics of the proposed method reach 95.74% and 1.9476 mm respectively, indicating that it is not only superior to the classical models such as UNet, PANet, PSPNet, UNet++, and DeepLabv3, but also show better performance than the newly-proposed SOTA (state-of-the-art) models such as CCNet, MANet, BiseNetv2, Swin-Unet, MedT, TransUNet, and UCTransNet.
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Affiliation(s)
- Zhuo Zhang
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, China
| | - Xiaochen Zhang
- Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, China
| | - Yong Yang
- School of Computer Science and Technology, Tiangong University, Tianjin, China
| | - Jieyu Liu
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, China
| | - Chenzi Zheng
- College of Foreign Languages, Nankai University, Tianjin, China
| | - Hua Bai
- Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronic and Information Engineering, Tiangong University, Tianjin, China
| | - Quanfeng Ma
- Tianjin Cerebral Vascular and Neural Degenerative Disease Key Laboratory, Tianjin Huanhu Hospital, Tianjin, China
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3
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Semi-supervised 3D brain tumor detection system using a tumor cut-based technique. Soft comput 2023. [DOI: 10.1007/s00500-023-07943-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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4
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Mahesh Kumar G, Parthasarathy E. Development of an enhanced U-Net model for brain tumor segmentation with optimized architecture. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
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5
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Wu N, Jia D, Zhang C, Li Z. Cervical cell classification based on strong feature CNN-LSVM network using Adaboost optimization. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Cervical cancer is one of the most common causes of death in women in the world, and early screening is an effective means of diagnosis and treatment, which can greatly improve the survival rate. Cervical cell classification model is an effective means to assist screening. However, the existing single model, including CNNs and machine learning methods, still has shortcomings such as unclear feature meaning, low accuracy and insufficient supervision. To solve the shortcomings of a single model, a novel framework based on strong feature Convolutional Neural Networks (CNN)-Lagrangian Support Vector Machine (LSVM) model is proposed for the accurate classification of cervical cells. Strong features extracted by hybrid methods are fused with the abstract ones from hidden layers of LeNet-5, then the fused features are processed with dimension reduction and fed into the LSVM classifier optimized by Adaboost for classification. Proposed model is evaluated using the augmented Herlev and private dataset with the metrics including accuracy (Acc), sensitivity (Sn), and specificity (Sp), which outperformed the baselines and state-of-the-art approaches with the Acc of 99.5% and 94.2% in 2&7-class classification, respectively.
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Affiliation(s)
- Nengkai Wu
- Beijing Jiaotong University, School of Electronics and Information Engineering, Beijing, China
| | - Dongyao Jia
- Beijing Jiaotong University, School of Electronics and Information Engineering, Beijing, China
| | - Chuanwang Zhang
- Beijing Jiaotong University, School of Electronics and Information Engineering, Beijing, China
| | - Ziqi Li
- Beijing Jiaotong University, School of Electronics and Information Engineering, Beijing, China
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6
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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
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7
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Anita JN, Kumaran S. A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation. J Cancer Prev 2022; 27:192-198. [PMID: 36258715 PMCID: PMC9537580 DOI: 10.15430/jcp.2022.27.3.192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 09/09/2022] [Accepted: 09/16/2022] [Indexed: 11/03/2022] Open
Abstract
The meningioma brain tumor detection and segmentation method is a complex process due to its low intensity pixel profile. In this article, the meningioma brain tumor images were detected and tumor regions were segmented using a convolutional neural network (CNN) classification approach. The source brain MRI images were decomposed using the discrete wavelet transform and these decomposed sub bands were fused using an arithmetic fusion technique. The fused image was data augmented in order to increase the sample size. The data augmented images were classified into either healthy or malignant using a CNN classifier. Then, the tumor region in the classified meningioma brain image was segmented using an connection component analysis algorithm. The tumor region segmented meningioma brain image was compressed using a lossless compression technique. The proposed method stated in this article was experimentally tested with the sets of meningioma brain images from an open access dataset. The experimental results were compared with existing methods in terms of sensitivity, specificity and tumor segmentation accuracy.
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Affiliation(s)
- John Nisha Anita
- Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, India,Correspondence to John Nisha Anita, E-mail: , https://orcid.org/0000-0003-4777-2123
| | - Sujatha Kumaran
- Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India
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Superlative Feature Selection Based Image Classification Using Deep Learning in Medical Imaging. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7028717. [PMID: 36199372 PMCID: PMC9529489 DOI: 10.1155/2022/7028717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/06/2022] [Accepted: 09/17/2022] [Indexed: 11/30/2022]
Abstract
Medical image recognition plays an essential role in the forecasting and early identification of serious diseases in the field of identification. Medical pictures are essential to a patient's health record since they may be used to control, manage, and treat illnesses. On the other hand, image categorization is a difficult problem in diagnostics. This paper provides an enhanced classifier based on the outstanding Feature Selection oriented Clinical Classifier using the Deep Learning (DL) model, which incorporates preprocessing, extraction of features, and classifying. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The proposed methodology is based on feature extraction with the pretrained EfficientNetB0 model. The optimum features enhanced the classifier performance and raised the precision, recall, F1 score, accuracy, and detection of medical pictures to improve the effectiveness of the DL classifier. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The optimum features enhanced the classifier performance and raised the result parameters for detecting medical pictures to improve the effectiveness of the DL classifier. Experiment findings reveal that our presented approach outperforms and achieves 98% accuracy.
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9
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Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10245-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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10
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Seg Net and Salp Water Optimization-driven Deep Belief network for segmentation and classification of brain tumor. Gene Expr Patterns 2022; 45:119248. [PMID: 35667619 DOI: 10.1016/j.gep.2022.119248] [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: 12/13/2021] [Revised: 03/19/2022] [Accepted: 05/28/2022] [Indexed: 11/21/2022]
Abstract
Classification of brain tumor in Magnetic Resonance Imaging (MRI) images is highly popular in treatment planning, early diagnosis, and outcome evaluation. It is very difficult for classifying and diagnosing tumors from several images. Thus, an automatic prediction strategy is essential in classifying brain tumors as malignant, core, edema, or benign. In this research, a novel approach using Salp Water Optimization-based Deep Belief network (SWO-based DBN) is introduced to classify brain tumor. At the initial stage, the input image is pre-processed to eradicate the artifacts present in input image. Following pre-processing, the segmentation is executed by SegNet, where the SegNet is trained using the proposed SWO. Moreover, the Convolutional Neural Network (CNN) features are employed to mine the features for future processing. At last, the introduced SWO-based DBN technique efficiently categorizes the brain tumor with respect to the extracted features. Thereafter, the produced output of the introduced SegNet + SWO-based DBN is made use of in brain tumor segmentation and classification. The developed technique produced better results with highest values of accuracy at 0.933, specificity at 0.880, and sensitivity at 0.938 using BRATS, 2018 datasets and accuracy at 0.921, specificity at 0.853, and sensitivity at 0.928 for BRATS, 2020 dataset.
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11
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Das S, Nayak GK, Saba L, Kalra M, Suri JS, Saxena S. An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Comput Biol Med 2022; 143:105273. [PMID: 35228172 DOI: 10.1016/j.compbiomed.2022.105273] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/15/2022] [Accepted: 01/24/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has become a prominent technique for medical diagnosis and represents an essential role in detecting brain tumors. Although AI-based models are widely used in brain lesion segmentation (BLS), understanding their effectiveness is challenging due to their complexity and diversity. Several reviews on brain tumor segmentation are available, but none of them describe a link between the threats due to risk-of-bias (RoB) in AI and its architectures. In our review, we focused on linking RoB and different AI-based architectural Cluster in popular DL framework. Further, due to variance in these designs and input data types in medical imaging, it is necessary to present a narrative review considering all facets of BLS. APPROACH The proposed study uses a PRISMA strategy based on 75 relevant studies found by searching PubMed, Scopus, and Google Scholar. Based on the architectural evolution, DL studies were subsequently categorized into four classes: convolutional neural network (CNN)-based, encoder-decoder (ED)-based, transfer learning (TL)-based, and hybrid DL (HDL)-based architectures. These studies were then analyzed considering 32 AI attributes, with clusters including AI architecture, imaging modalities, hyper-parameters, performance evaluation metrics, and clinical evaluation. Then, after these studies were scored for all attributes, a composite score was computed, normalized, and ranked. Thereafter, a bias cutoff (AP(ai)Bias 1.0, AtheroPoint, Roseville, CA, USA) was established to detect low-, moderate- and high-bias studies. CONCLUSION The four classes of architectures, from best-to worst-performing, are TL > ED > CNN > HDL. ED-based models had the lowest AI bias for BLS. This study presents a set of three primary and six secondary recommendations for lowering the RoB.
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Affiliation(s)
- Suchismita Das
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India; CSE Department, KIIT Deemed to be University, Bhubaneswar, Odisha, India
| | - G K Nayak
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India
| | - Luca Saba
- Department of Radiology, AOU, University of Cagliari, Cagliari, Italy
| | - Mannudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, USA.
| | - Sanjay Saxena
- CSE Department, International Institute of Information Technology, Bhubaneswar, Odisha, India
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12
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Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062900] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor.
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13
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A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis. J Pers Med 2022; 12:jpm12020275. [PMID: 35207763 PMCID: PMC8880689 DOI: 10.3390/jpm12020275] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 12/12/2022] Open
Abstract
Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.
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Sambath Kumar K, Rajendran A. An automatic brain tumor segmentation using modified inception module based U-Net model. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-211879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Manual segmentation of brain tumor is not only a tedious task that may bring human mistakes. An automatic segmentation gives results faster, and it extends the survival rate with an earlier treatment plan. So, an automatic brain tumor segmentation model, modified inception module based U-Net (IMU-Net) proposed. It takes Magnetic resonance (MR) images from the BRATS 2017 training dataset with four modalities (FLAIR, T1, T1ce, and T2). The concatenation of two series 3×3 kernels, one 5×5, and one 1×1 convolution kernels are utilized to extract the whole tumor (WT), core tumor (CT), and enhance tumor (ET). The modified inception module (IM) collects all the relevant features and provides better segmentation results. The proposed deep learning model contains 40 convolution layers and utilizes intensity normalization and data augmentation operation for further improvement. It achieved the mean dice similarity coefficient (DSC) of 0.90, 0.77, 0.74, and the mean Intersection over Union (IOU) of 0.79, 0.70, 0.70 for WT, CT, and ET during the evaluation.
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Affiliation(s)
- K. Sambath Kumar
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India
| | - A. Rajendran
- Department of Electronics and Communication Engineering, Karpagam College of Engineering, Myleripalayam Village, Othakalmandapam, Coimbatore, Tamilnadu, India
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15
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Sasank V, Venkateswarlu S. An automatic tumour growth prediction based segmentation using full resolution convolutional network for brain tumour. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103090] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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16
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Zhang Q, Yun KK, Wang H, Yoon SW, Lu F, Won D. Automatic cell counting from stimulated Raman imaging using deep learning. PLoS One 2021; 16:e0254586. [PMID: 34288972 PMCID: PMC8294532 DOI: 10.1371/journal.pone.0254586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 06/29/2021] [Indexed: 11/28/2022] Open
Abstract
In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor diagnosis and surgery by mapping lipids and proteins from fresh specimens and conducting a fast disclose of fundamental diagnostic hallmarks of tumors with a high resolution. However, cell counting from label-free SRS images has been challenging due to the limited contrast of cells and tissue, along with the heterogeneity of tissue morphology and biochemical compositions. To this end, a deep learning-based cell counting scheme is proposed by modifying and applying U-Net, an effective medical image semantic segmentation model that uses a small number of training samples. The distance transform and watershed segmentation algorithms are also implemented to yield the cell instance segmentation and cell counting results. By performing cell counting on SRS images of real human brain tumor specimens, promising cell counting results are obtained with > 98% of area under the curve (AUC) and R = 0.97 in terms of cell counting correlation between SRS and histological images with hematoxylin and eosin (H&E) staining. The proposed cell counting scheme illustrates the possibility and potential of performing cell counting automatically in near real time and encourages the study of applying deep learning techniques in biomedical and pathological image analyses.
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Affiliation(s)
- Qianqian Zhang
- Department of System Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, United States of America
| | - Kyung Keun Yun
- Department of System Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, United States of America
| | - Hao Wang
- Department of System Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, United States of America
| | - Sang Won Yoon
- Department of System Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, United States of America
| | - Fake Lu
- Department of Biomedical Engineering, State University of New York at Binghamton, Binghamton, NY, United States of America
| | - Daehan Won
- Department of System Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, United States of America
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17
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Zegers C, Posch J, Traverso A, Eekers D, Postma A, Backes W, Dekker A, van Elmpt W. Current applications of deep-learning in neuro-oncological MRI. Phys Med 2021; 83:161-173. [DOI: 10.1016/j.ejmp.2021.03.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 12/18/2022] Open
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18
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MRI brain tumor medical images analysis using deep learning techniques: a systematic review. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-020-00514-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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19
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Zadeh Shirazi A, Fornaciari E, McDonnell MD, Yaghoobi M, Cevallos Y, Tello-Oquendo L, Inca D, Gomez GA. The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey. J Pers Med 2020; 10:E224. [PMID: 33198332 PMCID: PMC7711876 DOI: 10.3390/jpm10040224] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/10/2020] [Accepted: 11/10/2020] [Indexed: 12/15/2022] Open
Abstract
In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.
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Affiliation(s)
- Amin Zadeh Shirazi
- Centre for Cancer Biology, SA Pathology and the University of South of Australia, Adelaide, SA 5000, Australia;
- Computational Learning Systems Laboratory, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia;
| | - Eric Fornaciari
- Department of Mathematics of Computation, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA;
| | - Mark D. McDonnell
- Computational Learning Systems Laboratory, UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia;
| | - Mahdi Yaghoobi
- Electrical and Computer Engineering Department, Islamic Azad University, Mashhad Branch, Mashad 917794-8564, Iran;
| | - Yesenia Cevallos
- College of Engineering, Universidad Nacional de Chimborazo, Riobamba 060150, Ecuador; (Y.C.); (L.T.-O.); (D.I.)
| | - Luis Tello-Oquendo
- College of Engineering, Universidad Nacional de Chimborazo, Riobamba 060150, Ecuador; (Y.C.); (L.T.-O.); (D.I.)
| | - Deysi Inca
- College of Engineering, Universidad Nacional de Chimborazo, Riobamba 060150, Ecuador; (Y.C.); (L.T.-O.); (D.I.)
| | - Guillermo A. Gomez
- Centre for Cancer Biology, SA Pathology and the University of South of Australia, Adelaide, SA 5000, Australia;
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20
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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21
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Nadeem MW, Ghamdi MAA, Hussain M, Khan MA, Khan KM, Almotiri SH, Butt SA. Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges. Brain Sci 2020; 10:brainsci10020118. [PMID: 32098333 PMCID: PMC7071415 DOI: 10.3390/brainsci10020118] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 02/07/2020] [Accepted: 02/13/2020] [Indexed: 12/17/2022] Open
Abstract
Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.
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Affiliation(s)
- Muhammad Waqas Nadeem
- Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan; (M.A.K.); (K.M.K.)
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54000, Pakistan;
- Correspondence:
| | - Mohammed A. Al Ghamdi
- Department of Computer Science, Umm Al-Qura University, Makkah 23500, Saudi Arabia; (M.A.A.G.); (S.H.A.)
| | - Muzammil Hussain
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54000, Pakistan;
| | - Muhammad Adnan Khan
- Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan; (M.A.K.); (K.M.K.)
| | - Khalid Masood Khan
- Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan; (M.A.K.); (K.M.K.)
| | - Sultan H. Almotiri
- Department of Computer Science, Umm Al-Qura University, Makkah 23500, Saudi Arabia; (M.A.A.G.); (S.H.A.)
| | - Suhail Ashfaq Butt
- Department of Information Sciences, Division of Science and Technology, University of Education Township, Lahore 54700, Pakistan;
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Zhang F, Wang Q, Li H. Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet. Technol Cancer Res Treat 2020. [PMCID: PMC7432983 DOI: 10.1177/1533033820947484] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Radiotherapy plays an important role in the treatment of non-small cell lung
cancer. Accurate segmentation of the gross target volume is very important for
successful radiotherapy delivery. Deep learning techniques can obtain fast and
accurate segmentation, which is independent of experts’ experience and saves
time compared with manual delineation. In this paper, we introduce a modified
version of ResNet and apply it to segment the gross target volume in computed
tomography images of patients with non-small cell lung cancer. Normalization was
applied to reduce the differences among images and data augmentation techniques
were employed to further enrich the data of the training set. Two different
residual convolutional blocks were used to efficiently extract the deep features
of the computed tomography images, and the features from all levels of the
ResNet were merged into a single output. This simple design achieved a fusion of
deep semantic features and shallow appearance features to generate dense pixel
outputs. The test loss tended to be stable after 50 training epochs, and the
segmentation took 21 ms per computed tomography image. The average evaluation
metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient,
0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results
were better than those of U-Net, which was used as a benchmark. The modified
ResNet directly extracted multi-scale context features from original input
images. Thus, the proposed automatic segmentation method can quickly segment the
gross target volume in non-small cell lung cancer cases and be applied to
improve consistency in contouring.
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Affiliation(s)
- Fuli Zhang
- Radiation Oncology Department, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Qiusheng Wang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Haipeng Li
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
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23
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Three-Phase Automatic Brain Tumor Diagnosis System Using Patches Based Updated Run Length Region Growing Technique. J Digit Imaging 2019; 33:465-479. [PMID: 31529237 DOI: 10.1007/s10278-019-00276-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Manually finding and segmenting brain tumor is a tedious process in MR brain images due to the unpredictable appearance of tissues with a different pattern, contour, mass, and positions. The proposed work has three phases automatic tumor diagnosis system for tumorous slice detection, segmentation, and visualization from MRI human head volumes. The proposed method has an automatic classification followed by segmentation and is called as patch-based updated run length region growing technique (PR2G). In the first phase, classification is done through training and testing process using SVM classifier with 8 × 8 patches. Three optimal features are chosen using infinite feature selection (IFS) method. The purpose of the first phase is to automatically cluster the input MRI image into a normal or tumorous slice and localize the tumor. The second phase aims to segment the tumor in abnormal tumorous slices identified by the first phase using run length region growing technique. Finally, the third phase contains a post metric evaluation like 3D tumor volume construction and estimation from actual and segmented tumor volume using Carelieri's estimator. Classification accuracy is measured using sensitivity, specificity, accuracy, and error rates also calculated using false alarm (FA) and missed alarm (MA). Segmentation accuracy is calculated using Dice similarity, positive predictive value (PPV), sensitivity, and accuracy. Datasets used for this experiment are collected from whole brain atlas (WBA) and BraTS repositories. Experimental results show that the PR2G achieves 97% of classification accuracy and 80% of Dice segmentation accuracy.
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Savaş S, Topaloğlu N, Kazcı Ö, Koşar PN. Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning. J Med Syst 2019; 43:273. [PMID: 31278481 DOI: 10.1007/s10916-019-1406-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 06/25/2019] [Indexed: 02/01/2023]
Abstract
Cerebrovascular accident due to carotid artery disease is the most common cause of death in developed countries following heart disease and cancer. For a reliable early detection of atherosclerosis, Intima Media Thickness (IMT) measurement and classification are important. A new method for decision support purpose for the classification of IMT was proposed in this study. Ultrasound images are used for IMT measurements. Images are classified and evaluated by experts. This is a manual procedure, so it causes subjectivity and variability in the IMT classification. Instead, this article proposes a methodology based on artificial intelligence methods for IMT classification. For this purpose, a deep learning strategy with multiple hidden layers has been developed. In order to create the proposed model, convolutional neural network algorithm, which is frequently used in image classification problems, is used. 501 ultrasound images from 153 patients were used to test the model. The images are classified by two specialists, then the model is trained and tested on the images, and the results are explained. The deep learning model in the study achieved an accuracy of 89.1% in the IMT classification with 89% sensitivity and 88% specificity. Thus, the assessments in this paper have shown that this methodology performs reasonable results for IMT classification.
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Affiliation(s)
- Serkan Savaş
- Faculty of Technology, Computer Engineering Department Ph.D, Gazi University, Ankara, Turkey.
| | - Nurettin Topaloğlu
- Faculty of Technology, Computer Engineering Department, Gazi University, Ankara, Turkey
| | - Ömer Kazcı
- Department of Radiology, Ankara Training and Research Hospital, Ankara, Turkey
| | - Pınar Nercis Koşar
- Department of Radiology, Ankara Training and Research Hospital, Ankara, Turkey
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