Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors.Computer-assisted image a...Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors.Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations.In this review,we discuss computer-based image processing methods using deep learning,convolutional neural networks(CNNs),radiomics,and transformer-based methods for kidney tumors.These techniques hold significant potential for automated segmentation,classification,and prognostic estimation with high accuracy,enabling more precise and personalized treatment planning.Special focus is given to Vision Transformers(ViTs),Explainable AI(XAI),Federated Learning(FL),and 3D kidney image analysis.Additionally,the strengths and limitations of the established models are compared with recent techniques to understand both clinical and computational challenges that remain unresolved.Finally,the future directions for enhancing diagnostic precision,streamlining physician workflows,and image-guided intervention for decision support are proposed.展开更多
Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.On...Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.Once the vision is lost,it cannot be regained but can be prevented from causing any further damage.Early diagnosis of DR is required for preventing vision loss,for which a trained ophthalmologist is required.The clinical practice is time-consuming and is not much successful in identifying DR at early stages.Hence,Computer-Aided Diagnosis(CAD)system is a suitable alternative for screening and grading of DR for a larger population.This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques.The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.展开更多
Digital Image Processing(DIP)is a well-developed field in the biological sciences which involves classification and detection of tumour.In medical science,automatic brain tumor diagnosis is an important phase.Brain tu...Digital Image Processing(DIP)is a well-developed field in the biological sciences which involves classification and detection of tumour.In medical science,automatic brain tumor diagnosis is an important phase.Brain tumor detection is performed by Computer-Aided Diagnosis(CAD)systems.The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes.Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research.Brain tumor diagnosis mainly performed for obtaining exact location,orientation and area of abnormal tissues.Cancer and edema regions inference from brain magnetic resonance imaging(MRI)information is considered to be great challenge due to brain tumors complex structure,blurred borders,besides exterior features like noise.The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation.Combined form of Median Modified Wiener filter(CMMWF)is chiefly deployed for denoising,and morphological operations which in turn eliminate nonbrain tissue,efficiently dropping technique’s sensitivity to noise.The proposed system contains the main phases such as preprocessing,brain tumor extraction and post processing.Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering(IPFC)algorithm.The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction.Then,the post processing of images are done through morphological operations along with Hybrid Median filtering(HMF)for attaining exact tumors representations.Additionally,suggested algorithm is substantiated by comparing with other existing segmentation algorithms.The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy,sensitivity,specificity,and recall.展开更多
文摘Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors.Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations.In this review,we discuss computer-based image processing methods using deep learning,convolutional neural networks(CNNs),radiomics,and transformer-based methods for kidney tumors.These techniques hold significant potential for automated segmentation,classification,and prognostic estimation with high accuracy,enabling more precise and personalized treatment planning.Special focus is given to Vision Transformers(ViTs),Explainable AI(XAI),Federated Learning(FL),and 3D kidney image analysis.Additionally,the strengths and limitations of the established models are compared with recent techniques to understand both clinical and computational challenges that remain unresolved.Finally,the future directions for enhancing diagnostic precision,streamlining physician workflows,and image-guided intervention for decision support are proposed.
文摘Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.Once the vision is lost,it cannot be regained but can be prevented from causing any further damage.Early diagnosis of DR is required for preventing vision loss,for which a trained ophthalmologist is required.The clinical practice is time-consuming and is not much successful in identifying DR at early stages.Hence,Computer-Aided Diagnosis(CAD)system is a suitable alternative for screening and grading of DR for a larger population.This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques.The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.
文摘Digital Image Processing(DIP)is a well-developed field in the biological sciences which involves classification and detection of tumour.In medical science,automatic brain tumor diagnosis is an important phase.Brain tumor detection is performed by Computer-Aided Diagnosis(CAD)systems.The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes.Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research.Brain tumor diagnosis mainly performed for obtaining exact location,orientation and area of abnormal tissues.Cancer and edema regions inference from brain magnetic resonance imaging(MRI)information is considered to be great challenge due to brain tumors complex structure,blurred borders,besides exterior features like noise.The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation.Combined form of Median Modified Wiener filter(CMMWF)is chiefly deployed for denoising,and morphological operations which in turn eliminate nonbrain tissue,efficiently dropping technique’s sensitivity to noise.The proposed system contains the main phases such as preprocessing,brain tumor extraction and post processing.Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering(IPFC)algorithm.The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction.Then,the post processing of images are done through morphological operations along with Hybrid Median filtering(HMF)for attaining exact tumors representations.Additionally,suggested algorithm is substantiated by comparing with other existing segmentation algorithms.The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy,sensitivity,specificity,and recall.
