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Hussain D, Al-Masni MA, Aslam M, Sadeghi-Niaraki A, Hussain J, Gu YH, Naqvi RA. Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:857-911. [PMID: 38701131 DOI: 10.3233/xst-230429] [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: 05/05/2024]
Abstract
BACKGROUND The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking. OBJECTIVE This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress. METHODS Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances accuracy and robustness. RESULTS Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT. FUTURE DIRECTIONS The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain. CONCLUSION Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice.
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Affiliation(s)
- Dildar Hussain
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Mohammed A Al-Masni
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Muhammad Aslam
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Abolghasem Sadeghi-Niaraki
- Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Korea
| | - Jamil Hussain
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Yeong Hyeon Gu
- Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea
| | - Rizwan Ali Naqvi
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, Korea
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Zheng B, Zhang M, Zhu S, Wu M, Chen L, Zhang S, Yang W. Research on an artificial intelligence-based myopic maculopathy grading method using EfficientNet. Indian J Ophthalmol 2024; 72:S53-S59. [PMID: 38131543 PMCID: PMC10833160 DOI: 10.4103/ijo.ijo_48_23] [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: 01/06/2023] [Revised: 08/04/2023] [Accepted: 08/15/2023] [Indexed: 12/23/2023] Open
Abstract
PURPOSE We aimed to develop an artificial intelligence-based myopic maculopathy grading method using EfficientNet to overcome the delayed grading and diagnosis of different myopic maculopathy degrees. METHODS The cooperative hospital provided 4642 healthy and myopic maculopathy color fundus photographs, comprising the four degrees of myopic maculopathy and healthy fundi. The myopic maculopathy grading models were trained using EfficientNet-B0 to EfficientNet-B7 models. The diagnostic results were compared with those of the VGG16 and ResNet50 classification models. The leading evaluation indicators were sensitivity, specificity, F1 score, area under the receiver operating characteristic (ROC) curve area under curve (AUC), 95% confidence interval, kappa value, and accuracy. The ROC curves of the ten grading models were also compared. RESULTS We used 1199 color fundus photographs to evaluate the myopic maculopathy grading models. The size of the EfficientNet-B0 myopic maculopathy grading model was 15.6 MB, and it had the highest kappa value (88.32%) and accuracy (83.58%). The model's sensitivities to diagnose tessellated fundus (TF), diffuse chorioretinal atrophy (DCA), patchy chorioretinal atrophy (PCA), and macular atrophy (MA) were 96.86%, 75.98%, 64.67%, and 88.75%, respectively. The specificity was above 93%, and the AUCs were 0.992, 0.960, 0.964, and 0.989, respectively. CONCLUSION The EfficientNet models were used to design grading diagnostic models for myopic maculopathy. Based on the collected fundus images, the models could diagnose a healthy fundus and four types of myopic maculopathy. The models might help ophthalmologists to make preliminary diagnoses of different degrees of myopic maculopathy.
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Affiliation(s)
- Bo Zheng
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Maotao Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Shaojun Zhu
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Maonian Wu
- School of Information Engineering, Huzhou University, Huzhou, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou, China
| | - Lu Chen
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | | | - Weihua Yang
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
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Albahli S, Nazir T. A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images. Bioengineering (Basel) 2023; 10:1147. [PMID: 37892876 PMCID: PMC10604551 DOI: 10.3390/bioengineering10101147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 10/29/2023] Open
Abstract
Signet ring cell (SRC) carcinoma is a particularly serious type of cancer that is a leading cause of death all over the world. SRC carcinoma has a more deceptive onset than other carcinomas and is mostly encountered in its later stages. Thus, the recognition of SRCs at their initial stages is a challenge because of different variants and sizes and illumination changes. The recognition process of SRCs at their early stages is costly because of the requirement for medical experts. A timely diagnosis is important because the level of the disease determines the severity, cure, and survival rate of victims. To tackle the current challenges, a deep learning (DL)-based methodology is proposed in this paper, i.e., custom CircleNet with ResNet-34 for SRC recognition and classification. We chose this method because of the circular shapes of SRCs and achieved better performance due to the CircleNet method. We utilized a challenging dataset for experimentation and performed augmentation to increase the dataset samples. The experiments were conducted using 35,000 images and attained 96.40% accuracy. We performed a comparative analysis and confirmed that our method outperforms the other methods.
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Affiliation(s)
- Saleh Albahli
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia;
| | - Tahira Nazir
- Faculty of Computing, Riphah International University, Islamabad 44600, Pakistan
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4
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Alammar Z, Alzubaidi L, Zhang J, Li Y, Lafta W, Gu Y. Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images. Cancers (Basel) 2023; 15:4007. [PMID: 37568821 PMCID: PMC10417687 DOI: 10.3390/cancers15154007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/29/2023] [Accepted: 08/05/2023] [Indexed: 08/13/2023] Open
Abstract
Medical image classification poses significant challenges in real-world scenarios. One major obstacle is the scarcity of labelled training data, which hampers the performance of image-classification algorithms and generalisation. Gathering sufficient labelled data is often difficult and time-consuming in the medical domain, but deep learning (DL) has shown remarkable performance, although it typically requires a large amount of labelled data to achieve optimal results. Transfer learning (TL) has played a pivotal role in reducing the time, cost, and need for a large number of labelled images. This paper presents a novel TL approach that aims to overcome the limitations and disadvantages of TL that are characteristic of an ImageNet dataset, which belongs to a different domain. Our proposed TL approach involves training DL models on numerous medical images that are similar to the target dataset. These models were then fine-tuned using a small set of annotated medical images to leverage the knowledge gained from the pre-training phase. We specifically focused on medical X-ray imaging scenarios that involve the humerus and wrist from the musculoskeletal radiographs (MURA) dataset. Both of these tasks face significant challenges regarding accurate classification. The models trained with the proposed TL were used to extract features and were subsequently fused to train several machine learning (ML) classifiers. We combined these diverse features to represent various relevant characteristics in a comprehensive way. Through extensive evaluation, our proposed TL and feature-fusion approach using ML classifiers achieved remarkable results. For the classification of the humerus, we achieved an accuracy of 87.85%, an F1-score of 87.63%, and a Cohen's Kappa coefficient of 75.69%. For wrist classification, our approach achieved an accuracy of 85.58%, an F1-score of 82.70%, and a Cohen's Kappa coefficient of 70.46%. The results demonstrated that the models trained using our proposed TL approach outperformed those trained with ImageNet TL. We employed visualisation techniques to further validate these findings, including a gradient-based class activation heat map (Grad-CAM) and locally interpretable model-independent explanations (LIME). These visualisation tools provided additional evidence to support the superior accuracy of models trained with our proposed TL approach compared to those trained with ImageNet TL. Furthermore, our proposed TL approach exhibited greater robustness in various experiments compared to ImageNet TL. Importantly, the proposed TL approach and the feature-fusion technique are not limited to specific tasks. They can be applied to various medical image applications, thus extending their utility and potential impact. To demonstrate the concept of reusability, a computed tomography (CT) case was adopted. The results obtained from the proposed method showed improvements.
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Affiliation(s)
- Zaenab Alammar
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia; (J.Z.); (Y.L.)
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Laith Alzubaidi
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia;
- ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Jinglan Zhang
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia; (J.Z.); (Y.L.)
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia; (J.Z.); (Y.L.)
| | | | - Yuantong Gu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia;
- ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
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Retinal image blood vessel classification using hybrid deep learning in cataract diseased fundus images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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6
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Manikandan S, Raman R, Rajalakshmi R, Tamilselvi S, Surya RJ. Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis. Indian J Ophthalmol 2023; 71:1783-1796. [PMID: 37203031 PMCID: PMC10391382 DOI: 10.4103/ijo.ijo_2614_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023] Open
Abstract
Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94-0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90-0.96).
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Affiliation(s)
- Suchetha Manikandan
- Professor & Deputy Director, Centre for Healthcare Advancement, Innovation ! Research, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Rajiv Raman
- Senior Consultant, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Ramachandran Rajalakshmi
- Head Medical Retina, Dr. Mohan's Diabetes Specialties Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
| | - S Tamilselvi
- Junior Research Fellow, Centre for Healthcare Advancement, Innovation & Research, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - R Janani Surya
- Research Associate, Vision Research Foundation, Chennai, Tamil Nadu, India
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Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13051001. [PMID: 36900145 PMCID: PMC10000375 DOI: 10.3390/diagnostics13051001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/09/2023] Open
Abstract
Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.
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Li M, Liu S, Wang Z, Li X, Yan Z, Zhu R, Wan Z. MyopiaDETR: End-to-end pathological myopia detection based on transformer using 2D fundus images. Front Neurosci 2023; 17:1130609. [PMID: 36824210 PMCID: PMC9941630 DOI: 10.3389/fnins.2023.1130609] [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: 12/23/2022] [Accepted: 01/23/2023] [Indexed: 02/09/2023] Open
Abstract
Background Automated diagnosis of various retinal diseases based on fundus images can serve as an important clinical decision aid for curing vision loss. However, developing such an automated diagnostic solution is challenged by the characteristics of lesion area in 2D fundus images, such as morphology irregularity, imaging angle, and insufficient data. Methods To overcome those challenges, we propose a novel deep learning model named MyopiaDETR to detect the lesion area of normal myopia (NM), high myopia (HM) and pathological myopia (PM) using 2D fundus images provided by the iChallenge-PM dataset. To solve the challenge of morphology irregularity, we present a novel attentional FPN architecture and generate multi-scale feature maps to a traditional Detection Transformer (DETR) for detecting irregular lesion more accurate. Then, we choose the DETR structure to view the lesion from the perspective of set prediction and capture better global information. Several data augmentation methods are used on the iChallenge-PM dataset to solve the challenge of insufficient data. Results The experimental results demonstrate that our model achieves excellent localization and classification performance on the iChallenge-PM dataset, reaching AP50 of 86.32%. Conclusion Our model is effective to detect lesion areas in 2D fundus images. The model not only achieves a significant improvement in capturing small objects, but also a significant improvement in convergence speed during training.
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Affiliation(s)
- Manyu Li
- School of Information Engineering, Nanchang University, Jiangxi, China
| | - Shichang Liu
- School of Computer Science, Shaanxi Normal University, Xi’an, China
| | - Zihan Wang
- School of Information Engineering, Nanchang University, Jiangxi, China
| | - Xin Li
- School of Computer Science, Shaanxi Normal University, Xi’an, China
| | - Zezhong Yan
- School of Information Engineering, Nanchang University, Jiangxi, China
| | - Renping Zhu
- School of Information Engineering, Nanchang University, Jiangxi, China,Industrial Institute of Artificial Intelligence, Nanchang University, Jiangxi, China,School of Information Management, Wuhan University, Hubei, China,*Correspondence: Renping Zhu,
| | - Zhijiang Wan
- School of Information Engineering, Nanchang University, Jiangxi, China,Industrial Institute of Artificial Intelligence, Nanchang University, Jiangxi, China,Zhijiang Wan,
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Nage P, Shitole S, Kokare M. An intelligent approach for detection and grading of diabetic retinopathy and diabetic macular edema using retinal images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2023. [DOI: 10.1080/21681163.2022.2164358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Pranoti Nage
- Computer Science & Technology, Usha Mittal Institute of Technology for Women, S.N.D.T. Women’s University, Mumbai, India
| | - Sanjay Shitole
- Information Technology, Usha Mittal Institute of Technology for Women, S.N.D.T. Women’s University, Mumbai, India
| | - Manesh Kokare
- Centre of Excellence in Signal & Image Processing, Shri Guru Gobind Singhji Institute of Technology, Nanded, India
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Nawaz M, Nazir T, Khan MA, Alhaisoni M, Kim JY, Nam Y. MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy K-Means Clustering. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7502504. [PMID: 36276999 PMCID: PMC9586776 DOI: 10.1155/2022/7502504] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 09/17/2022] [Indexed: 11/18/2022]
Abstract
Melanoma is a dangerous form of skin cancer that results in the demise of patients at the developed stage. Researchers have attempted to develop automated systems for the timely recognition of this deadly disease. However, reliable and precise identification of melanoma moles is a tedious and complex activity as there exist huge differences in the mass, structure, and color of the skin lesions. Additionally, the incidence of noise, blurring, and chrominance changes in the suspected images further enhance the complexity of the detection procedure. In the proposed work, we try to overcome the limitations of the existing work by presenting a deep learning (DL) model. Descriptively, after accomplishing the preprocessing task, we have utilized an object detection approach named CornerNet model to detect melanoma lesions. Then the localized moles are passed as input to the fuzzy K-means (FLM) clustering approach to perform the segmentation task. To assess the segmentation power of the proposed approach, two standard databases named ISIC-2017 and ISIC-2018 are employed. Extensive experimentation has been conducted to demonstrate the robustness of the proposed approach through both numeric and pictorial results. The proposed approach is capable of detecting and segmenting the moles of arbitrary shapes and orientations. Furthermore, the presented work can tackle the presence of noise, blurring, and brightness variations as well. We have attained the segmentation accuracy values of 99.32% and 99.63% over the ISIC-2017 and ISIC-2018 databases correspondingly which clearly depicts the effectiveness of our model for the melanoma mole segmentation.
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Affiliation(s)
- Marriam Nawaz
- Department of Software Engineering, University of Engineering and Technology Taxila, 47050, Pakistan
- Department of Computer Science, University of Engineering and Technology Taxila, 47050, Pakistan
| | - Tahira Nazir
- Department of Computing, Riphah International University, Islamabad, Pakistan
| | | | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Jung-Yeon Kim
- Department of ICT Convergence, Soonchunhyang University, Asan 31538, Republic of Korea
| | - Yunyoung Nam
- Department of ICT Convergence, Soonchunhyang University, Asan 31538, Republic of Korea
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Panda NR, Sahoo AK. A Detailed Systematic Review on Retinal Image Segmentation Methods. J Digit Imaging 2022; 35:1250-1270. [PMID: 35508746 PMCID: PMC9582172 DOI: 10.1007/s10278-022-00640-9] [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: 01/09/2021] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 11/27/2022] Open
Abstract
The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent further impacts due to diabetes and hypertension. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on retinal segmentation review has revealed distinct methodologies with diverse frameworks that are utilized for blood vessel separation. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, machine learning (ML) and deep learning (DL) were compared and have been reported as the best model. Moreover, different datasets were used to segment the retinal blood vessels. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, REFUGE, and CHASE. This article discloses the implementation capacity of distinct techniques implemented for each segmentation method. Finally, the finest accuracy of 98% and sensitivity of 96% were achieved for the technique of Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM). Moreover, this technique has utilized public datasets to verify efficiency. Hence, the overall review of this article has revealed a method for earlier diagnosis of diseases to deliver earlier therapy.
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Affiliation(s)
- Nihar Ranjan Panda
- Department of Electronics and Communication Engineering, Silicon Institute of Technology, Bhubaneswar, Orissa, 751024, India.
| | - Ajit Kumar Sahoo
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India
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Albahli S, Nawaz M. DCNet: DenseNet-77-based CornerNet model for the tomato plant leaf disease detection and classification. FRONTIERS IN PLANT SCIENCE 2022; 13:957961. [PMID: 36160977 PMCID: PMC9499263 DOI: 10.3389/fpls.2022.957961] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/12/2022] [Indexed: 06/16/2023]
Abstract
Early recognition of tomato plant leaf diseases is mandatory to improve the food yield and save agriculturalists from costly spray procedures. The correct and timely identification of several tomato plant leaf diseases is a complicated task as the healthy and affected areas of plant leaves are highly similar. Moreover, the incidence of light variation, color, and brightness changes, and the occurrence of blurring and noise on the images further increase the complexity of the detection process. In this article, we have presented a robust approach for tackling the existing issues of tomato plant leaf disease detection and classification by using deep learning. We have proposed a novel approach, namely the DenseNet-77-based CornerNet model, for the localization and classification of the tomato plant leaf abnormalities. Specifically, we have used the DenseNet-77 as the backbone network of the CornerNet. This assists in the computing of the more nominative set of image features from the suspected samples that are later categorized into 10 classes by the one-stage detector of the CornerNet model. We have evaluated the proposed solution on a standard dataset, named PlantVillage, which is challenging in nature as it contains samples with immense brightness alterations, color variations, and leaf images with different dimensions and shapes. We have attained an average accuracy of 99.98% over the employed dataset. We have conducted several experiments to assure the effectiveness of our approach for the timely recognition of the tomato plant leaf diseases that can assist the agriculturalist to replace the manual systems.
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Affiliation(s)
- Saleh Albahli
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
| | - Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology–Taxila, Taxila, Pakistan
- Department of Software Engineering, University of Engineering and Technology–Taxila, Taxila, Pakistan
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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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14
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A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique. Med Biol Eng Comput 2022; 60:2015-2038. [PMID: 35545738 PMCID: PMC9225981 DOI: 10.1007/s11517-022-02564-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 03/25/2022] [Indexed: 12/23/2022]
Abstract
Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medical image analysis. Deep convolution neural network (CNN) architectures are widely used in multi-label (ML) classification. It helps in diagnosing normal and various DR grades: mild, moderate, and severe non-proliferative DR (NPDR) and proliferative DR (PDR). DR grades are formulated by appearing multiple DR lesions simultaneously on the color retinal fundus images. Many lesion types have various features that are difficult to segment and distinguished by utilizing conventional and hand-crafted methods. Therefore, the practical solution is to utilize an effective CNN model. In this paper, we present a novel hybrid, deep learning technique, which is called E-DenseNet. We integrated EyeNet and DenseNet models based on transfer learning. We customized the traditional EyeNet by inserting the dense blocks and optimized the resulting hybrid E-DensNet model's hyperparameters. The proposed system based on the E-DenseNet model can accurately diagnose healthy and different DR grades from various small and large ML color fundus images. We trained and tested our model on four different datasets that were published from 2006 to 2019. The proposed system achieved an average accuracy (ACC), sensitivity (SEN), specificity (SPE), Dice similarity coefficient (DSC), the quadratic Kappa score (QKS), and the calculation time (T) in minutes (m) equal [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], 0.883, and 3.5m respectively. The experiments show promising results as compared with other systems.
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Cao P, Hou Q, Song R, Wang H, Zaiane O. Collaborative learning of weakly-supervised domain adaptation for diabetic retinopathy grading on retinal images. Comput Biol Med 2022; 144:105341. [PMID: 35279423 DOI: 10.1016/j.compbiomed.2022.105341] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/20/2022] [Accepted: 02/20/2022] [Indexed: 11/25/2022]
Abstract
Early detection and treatment of diabetic retinopathy (DR) can significantly reduce the risk of vision loss in patients. In essence, we are faced with two challenges: (i) how to simultaneously achieve domain adaptation from the different domains and (ii) how to build an interpretable multi-instance learning (MIL) on the target domain in an end-to-end framework. In this paper, we address these issues and propose a unified weakly-supervised domain adaptation framework, which consists of three components: domain adaptation, instance progressive discriminator and multi-instance learning with attention. The method models the relationship between the patches and images in the target domain with a multi-instance learning scheme and an attention mechanism. Meanwhile, it incorporates all available information from both source and target domains for a jointly learning strategy. We validate the performance of the proposed framework for DR grading on the Messidor dataset and the large-scale Eyepacs dataset. The experimental results demonstrate that it achieves an average accuracy of 0.949 (95% CI 0.931-0.958)/0.764 (95% CI 0.755-0.772) and an average AUC value of 0.958 (95% CI 0.945-0.962)/0.749 (95% CI 0.732-0.761) for binary-class/multi-class classification tasks on the Messidor dataset. Moreover, the proposed method achieves an accuracy of 0.887 and a quadratic weighted kappa score value of 0.860 on the Eyepacs dataset, outperforming the state-of-the-art approaches. Comprehensive experiments confirm the effectiveness of the approach in terms of both grading performance and interpretability. The source code is available at https://github.com/HouQingshan/WAD-Net.
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Affiliation(s)
- Peng Cao
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China.
| | - Qingshan Hou
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Ruoxian Song
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Haonan Wang
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Osmar Zaiane
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Alberta, Canada
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16
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Zhao J, Lu Y, Qian Y, Luo Y, Yang W. Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: A Bibliometric and Visualized Study (Preprint). J Med Internet Res 2022; 24:e37532. [PMID: 35700021 PMCID: PMC9240965 DOI: 10.2196/37532] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/25/2022] [Accepted: 04/04/2022] [Indexed: 01/28/2023] Open
Abstract
Background Patients with retinal diseases may exhibit serious complications that cause severe visual impairment owing to a lack of awareness of retinal diseases and limited medical resources. Understanding how artificial intelligence (AI) is used to make predictions and perform relevant analyses is a very active area of research on retinal diseases. In this study, the relevant Science Citation Index (SCI) literature on the AI of retinal diseases published from 2012 to 2021 was integrated and analyzed. Objective The aim of this study was to gain insights into the overall application of AI technology to the research of retinal diseases from set time and space dimensions. Methods Citation data downloaded from the Web of Science Core Collection database for AI in retinal disease publications from January 1, 2012, to December 31, 2021, were considered for this analysis. Information retrieval was analyzed using the online analysis platforms of literature metrology: Bibliometrc, CiteSpace V, and VOSviewer. Results A total of 197 institutions from 86 countries contributed to relevant publications; China had the largest number and researchers from University College London had the highest H-index. The reference clusters of SCI papers were clustered into 12 categories. “Deep learning” was the cluster with the widest range of cocited references. The burst keywords represented the research frontiers in 2018-2021, which were “eye disease” and “enhancement.” Conclusions This study provides a systematic analysis method on the literature regarding AI in retinal diseases. Bibliometric analysis enabled obtaining results that were objective and comprehensive. In the future, high-quality retinal image–forming AI technology with strong stability and clinical applicability will continue to be encouraged.
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Affiliation(s)
- Junqiang Zhao
- Department of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China
- Department of Nursing, Xinxiang Medical University, Xinxiang, Henan, China
| | - Yi Lu
- Department of Nursing, Xinxiang Medical University, Xinxiang, Henan, China
| | - Yong Qian
- Jiangsu Testing and Inspection Institute for Medical Devices, Nanjing, Jiangsu, China
| | - Yuxin Luo
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Weihua Yang
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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17
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Nawaz M, Nazir T, Javed A, Tariq U, Yong HS, Khan MA, Cha J. An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization. SENSORS (BASEL, SWITZERLAND) 2022; 22:434. [PMID: 35062405 PMCID: PMC8780798 DOI: 10.3390/s22020434] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 12/29/2021] [Accepted: 01/05/2022] [Indexed: 02/04/2023]
Abstract
Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.
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Affiliation(s)
- Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology Taxila, Rawalpindi 47050, Pakistan; (M.N.); (T.N.); (A.J.)
| | - Tahira Nazir
- Department of Computer Science, University of Engineering and Technology Taxila, Rawalpindi 47050, Pakistan; (M.N.); (T.N.); (A.J.)
| | - Ali Javed
- Department of Computer Science, University of Engineering and Technology Taxila, Rawalpindi 47050, Pakistan; (M.N.); (T.N.); (A.J.)
| | - Usman Tariq
- Information Systems Department, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Khraj 11942, Saudi Arabia;
| | - Hwan-Seung Yong
- Department of Computer Science and Engineering, Ewha Womans University, Seoul 03760, Korea;
| | | | - Jaehyuk Cha
- Department of Computer Science, Hanyang University, Seoul 04763, Korea;
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18
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Faheem Saleem M, Muhammad Adnan Shah S, Nazir T, Mehmood A, Nawaz M, Attique Khan M, Kadry S, Majumdar A, Thinnukool O. Signet Ring Cell Detection from Histological Images Using Deep Learning. COMPUTERS, MATERIALS & CONTINUA 2022; 72:5985-5997. [DOI: 10.32604/cmc.2022.023101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/29/2021] [Indexed: 08/25/2024]
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19
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Dharejo FA, Deeba F, Zhou Y, Das B, Jatoi MA, Zawish M, Du Y, Wang X. TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution. ACM T INTEL SYST TEC 2021. [DOI: 10.1145/3456726] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Single Image Super-resolution (SISR)
produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and
generative adversarial networks (GANs)
have made breakthroughs for the challenging task of
single image super-resolution (SISR)
. However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating
Wavelet Transform (WT)
characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.
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Affiliation(s)
- Fayaz Ali Dharejo
- Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Farah Deeba
- Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Bhagwan Das
- Department of Electronic Engineering, Quaid-e-Awam University Engineering Science and Technology, Nawasbshah, Sindh, Pakistan
| | - Munsif Ali Jatoi
- Department of Biomedical Engineering, Salim Habib University, Karachi, Sindh, Pakistan
| | - Muhammad Zawish
- Walton Institute for Information and Communication Systems Science, Waterford, Ireland
| | - Yi Du
- Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xuezhi Wang
- Computer Network Information Center, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
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20
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Nazir T, Nawaz M, Rashid J, Mahum R, Masood M, Mehmood A, Ali F, Kim J, Kwon HY, Hussain A. Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model. SENSORS 2021; 21:s21165283. [PMID: 34450729 PMCID: PMC8398326 DOI: 10.3390/s21165283] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023]
Abstract
Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.
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Affiliation(s)
- Tahira Nazir
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Junaid Rashid
- Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;
- Correspondence: (J.R.); (H.-Y.K.)
| | - Rabbia Mahum
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Momina Masood
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Awais Mehmood
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Farooq Ali
- Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan; (T.N.); (M.N.); (R.M.); (M.M.); (A.M.); (F.A.)
| | - Jungeun Kim
- Department of Computer Science and Engineering, Kongju National University, Gongju 31080, Chungcheongnam-do, Korea;
| | - Hyuk-Yoon Kwon
- Research Center for Electrical and Information Technology, Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
- Correspondence: (J.R.); (H.-Y.K.)
| | - Amir Hussain
- Centre of AI and Data Science, Edinburgh Napier University, Edinburgh EH11 4DY, UK;
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21
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Masood M, Nazir T, Nawaz M, Mehmood A, Rashid J, Kwon HY, Mahmood T, Hussain A. A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images. Diagnostics (Basel) 2021; 11:diagnostics11050744. [PMID: 33919358 PMCID: PMC8143310 DOI: 10.3390/diagnostics11050744] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/18/2021] [Accepted: 04/19/2021] [Indexed: 01/15/2023] Open
Abstract
A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.
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Affiliation(s)
- Momina Masood
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.M.); (T.N.); (M.N.); (A.M.)
| | - Tahira Nazir
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.M.); (T.N.); (M.N.); (A.M.)
| | - Marriam Nawaz
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.M.); (T.N.); (M.N.); (A.M.)
| | - Awais Mehmood
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan; (M.M.); (T.N.); (M.N.); (A.M.)
| | - Junaid Rashid
- Department of Computer Science, AIR University Islamabad, Aerospace and Aviation Campus Kamra, Kamra 43570, Pakistan
- Correspondence: (J.R.); (H.-Y.K.)
| | - Hyuk-Yoon Kwon
- Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
- Correspondence: (J.R.); (H.-Y.K.)
| | - Toqeer Mahmood
- Department of Computer Science, National Textile University, Faisalabad 37610, Pakistan;
| | - Amir Hussain
- Data Science and Cyber Analytics Research Group, Edinburgh Napier University, Edinburgh EH11 4DY, UK;
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22
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Noman SM, Arshad J, Zeeshan M, Rehman AU, Haider A, Khurram S, Cheikhrouhou O, Hamam H, Shafiq M. An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18073755. [PMID: 33916851 PMCID: PMC8038424 DOI: 10.3390/ijerph18073755] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/19/2021] [Accepted: 03/23/2021] [Indexed: 02/05/2023]
Abstract
Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined toward the medical aspect of diabetes that neglects psychosocial care. In this retrospective study, specific distress was measured by the Diabetes Distress Screening (DDS) scale, and depression was analyzed by the Beck Depression Inventory (BDI) and Diagnosis Statistics Manual (DSM) criteria in type 2 diabetes mellitus (T2DM) patients of Northwestern Nigeria. Additionally, we applied the Chi-square test and linear regression to measure the forecast prevalence ratio and evaluate the link between the respective factors that further determine the odd ratios and coefficient correlations in five nonintrusive variables, namely age, gender, physical exercise, diabetes history, and smoking. In total, 712 sample patients were taken, with 51.68% male and 47.31% female patients. The mean age and body mass index (BMI) was 48.6 years ± 12.8 and 45.6 years ± 8.3. Based on the BDI prediction, 90.15% of patients were found depressed according to the DSM parameters, and depression prevalence was recorded around 22.06%. Overall, 88.20% of patients had DDS-dependent diabetes-specific distress with a prevalence ratio of 24.08%, of whom 45.86% were moderate and 54.14% serious. In sharp contrast, emotion-related distress of 28.96% was found compared to interpersonal (23.61%), followed by physician (16.42%) and regimen (13.21%) distress. The BDI-based matching of depression signs was also statistically significant with p < 0.001 in severe distress patients. However, 10.11% of patients were considered not to be depressed by DSM guidelines. The statistical evidence indicates that depression and distress are closely correlated with age, sex, diabetes history, physical exercise, and smoking influences. The facts and findings in this work show that emotional distress was found more prevalent. This study is significant because it considered several sociocultural and religious differences between Nigeria and large, undeveloped, populated countries with low socioeconomic status and excessive epidemiological risk. Finally, it is important for the clinical implications of T2DM patients on their initial screenings.
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Affiliation(s)
- Sohail M. Noman
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou 515041, China;
| | - Jehangir Arshad
- Department of Electrical & Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan;
| | - Muhammad Zeeshan
- Department of Medicine and Surgery, Al-Nafees Medical College and Hospital, Isra University, Islamabad 44000, Pakistan;
| | - Ateeq Ur Rehman
- Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan;
| | - Amir Haider
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea;
| | - Shahzada Khurram
- Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan;
| | - Omar Cheikhrouhou
- College of CIT, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Habib Hamam
- Faculty of Engineering, Moncton University, Moncton, NB E1A3E9, Canada;
| | - Muhammad Shafiq
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
- Correspondence:
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23
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Alzubaidi L, Al-Amidie M, Al-Asadi A, Humaidi AJ, Al-Shamma O, Fadhel MA, Zhang J, Santamaría J, Duan Y. Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data. Cancers (Basel) 2021; 13:1590. [PMID: 33808207 PMCID: PMC8036379 DOI: 10.3390/cancers13071590] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 03/24/2021] [Accepted: 03/27/2021] [Indexed: 12/27/2022] Open
Abstract
Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes-either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.
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Affiliation(s)
- Laith Alzubaidi
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia;
- AlNidhal Campus, University of Information Technology & Communications, Baghdad 10001, Iraq;
| | - Muthana Al-Amidie
- Faculty of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211, USA; (M.A.-A.); (A.A.-A.); (Y.D.)
| | - Ahmed Al-Asadi
- Faculty of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211, USA; (M.A.-A.); (A.A.-A.); (Y.D.)
| | - Amjad J. Humaidi
- Control and Systems Engineering Department, University of Technology, Baghdad 10001, Iraq;
| | - Omran Al-Shamma
- AlNidhal Campus, University of Information Technology & Communications, Baghdad 10001, Iraq;
| | - Mohammed A. Fadhel
- College of Computer Science and Information Technology, University of Sumer, Thi Qar 64005, Iraq;
| | - Jinglan Zhang
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia;
| | - J. Santamaría
- Department of Computer Science, University of Jaén, 23071 Jaén, Spain;
| | - Ye Duan
- Faculty of Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211, USA; (M.A.-A.); (A.A.-A.); (Y.D.)
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24
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Learning Medical Image Denoising with Deep Dynamic Residual Attention Network. MATHEMATICS 2020. [DOI: 10.3390/math8122192] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Image denoising performs a prominent role in medical image analysis. In many cases, it can drastically accelerate the diagnostic process by enhancing the perceptual quality of noisy image samples. However, despite the extensive practicability of medical image denoising, the existing denoising methods illustrate deficiencies in addressing the diverse range of noise appears in the multidisciplinary medical images. This study alleviates such challenging denoising task by learning residual noise from a substantial extent of data samples. Additionally, the proposed method accelerates the learning process by introducing a novel deep network, where the network architecture exploits the feature correlation known as the attention mechanism and combines it with spatially refine residual features. The experimental results illustrate that the proposed method can outperform the existing works by a substantial margin in both quantitative and qualitative comparisons. Also, the proposed method can handle real-world image noise and can improve the performance of different medical image analysis tasks without producing any visually disturbing artefacts.
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