Convolutional Neural Network in Medical Image Analysis: A Review

Kshatri, Sapna Singh; Singh, Deepak Kumar

doi:10.1007/s11831-023-09898-w

Cited by 41 publications

(14 citation statements)

References 144 publications

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“…Recent years have impressively shown the versatile applications CNNs can have in biomedical image analysis. 10 , 18 Of the 23 studies reviewed by Petersen and colleagues, only two studies employed this technology to the question of PCNSL/GBM distinction. 14 Yamashita and colleagues trained a neural network with 126 cases, among those 58 high-grade gliomas and 12 PCNSL.…”

Section: Discussionmentioning

confidence: 99%

“…Advances in biomedical image processing have impressively proven the versatile use cases for computer programs to analyze and evaluate medical images. 10 The current study, therefore, aimed to develop an automated diagnostic workflow to distinguish between PCNSL and GBM, to help clinicians decide which patients to administer antiedematous corticosteroid therapy.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning aided preoperative diagnosis of primary central nervous system lymphoma

Naser,

Maurer,

Fischer

et al. 2024

iScience

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning aided preoperative diagnosis of primary central nervous system lymphoma

Naser,

Maurer,

Fischer

et al. 2024

iScience

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“…In medical image analysis, DL techniques-particularly CNNs-are essential for MRI segmentation. Techniques like data augmentation and transfer learning are used to overcome problems like data scarcity, which eventually improves patient care outcomes and advances the area of medical imaging [22]. Wenjing Jiang et al introduced a novel ASD classi cation network called CNNG, combining Convolutional Neural Network and Gate Recurrent Unit to detect ASD [23].…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

A Dual Track Feature Fusion Network for ASD Detection using Swin Transformers and Convolutional Neural Network

Radhakrishnan,

Ramamurthy,

Shanmugam

et al. 2024

Preprint

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Early diagnosis of Autism Spectrum Disorder (ASD) plays a crucial role in enriching a child's development, particularly in improving social communication, language development, and addressing behavioural challenges. Early signs of autism may be observable in childhood, but a formal diagnosis often occurs later in life. Behavioural-based assessments, such as the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule-Revised (ADOS-R), are currently used for diagnosing ASD. These methods of diagnosis are time-consuming and require trained professionals. Due to these disadvantages of the traditional method of diagnosis, deep learning is used, where feature extraction is done automatically from Magnetic Resonance Imaging (MRI) data, eliminating the reliance on subjective pre-defined features. This advancement not only captures subtle information that may be missed by human-defined features but also enhances accuracy significantly. The dataset comprises of axial view of MRI images from ABIDE-I dataset from Autism Brain Imaging Data Exchange (ABIDE) database. This study proposes a dual-track feature fusion network architecture comprising Swin Transformer and customised Convolutional Neural Network (CNN) for precise classification. Swin Transformers excel in capturing long-range dependencies within images, facilitating a deeper understanding of interrelations among different image components. Concurrently, CNNs are adept at extracting local features, thus contributing to improved classification performance by considering both local and global features. The experimental outcomes highlight the efficacy of the proposed feature fusion network, showcasing an accuracy rate of 98.7%, precision of 98.12%, recall of 98.77%, and an F1-score of 98.65% upon evaluation using the ABIDE dataset.

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“…The potential application of Artificial Intelligence (AI) in medicine has been increasingly explored in recent years ( 1 ). The ability of convolutional neural networks (CNN) to recognize patterns in images for medical diagnosis has surpassed the accuracy of clinical specialists in experimental settings in various medical fields ( 2 ). To date algorithms have been successfully applied to image analysis for radiology ( 3 ), cardiology ( 4 ), ophthalmology ( 5 ), and dermatology ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

Primiero,

Betz-Stablein,

Ascott

et al. 2024

Front. Med.

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IntroductionArtificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with ‘untrained’ or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process.MethodsThis protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data.ConclusionThe anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.

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Convolutional Neural Network in Medical Image Analysis: A Review

Cited by 41 publications

References 144 publications

Deep learning aided preoperative diagnosis of primary central nervous system lymphoma

Deep learning aided preoperative diagnosis of primary central nervous system lymphoma

A Dual Track Feature Fusion Network for ASD Detection using Swin Transformers and Convolutional Neural Network

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification

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