Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs

Fujita, Hiroshi; Uchiyama, Yoshikazu; Nakagawa, Toshiaki; Fukuoka, Daisuke; Hatanaka, Yuji; Hara, Takeshi; Hayashi, Yasuhiro; Ikedo, Yuji; Gao, Xin; Zhou, Xiangrong

doi:10.1016/j.cmpb.2008.04.003

Cited by 95 publications

(42 citation statements)

References 22 publications

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“…Many authors have proposed various algorithms for classification of brain tumors by developing various computer aided diagnostic (CAD) system based on different features and classification algorithm [1][2][3][4]. Generally, these systems use image processing techniques such as feature extraction, selection, and classification.…”

Section: Background Theorymentioning

confidence: 99%

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Tiwari¹,

Sachdeva²,

Ahuja³

et al. 2017

IJCIS

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The iso, hypo or hyper intensity, similarity of shape, size and location complicates the identification of brain tumors. Therefore, an adequate Computer Aided Diagnosis (CAD) system is designed for classification of brain tumor for assisting inexperience radiologists in diagnosis process. A multifarious database of real post contrast T1-weighted MR images from 10 patients has been taken. This database consists of primary brain tumors namely Meningioma (MENI-class 1), Astrocytoma (AST-class 2), and Normal brain regions (NORM-class 3). The region of interest(s) (ROIs) of size 20 x 20 is extracted by the radiologists from each image in the database. A total of 371 texture and intensity features are extracted from these ROI(s). An Artificial Neural Network (ANN) is used to classify these three classes as it shows better classification results on multivariate non-linear, complicated, rule based domains, and decision making domains. It is being observed that ANN provides much accurate results in terms of individual classification accuracy and overall classification accuracy. The four discrete experiments have been performed. Initially, the experiment was performed by extracting 263 features and an overall classification accuracy 78.10% is achieved, however, it was noticed that MENI (class-1) was highly misclassified with AST (class-2). Further, to improve the overall classification accuracy and individual classification accuracy specifically for MENI (class-1), LAWs textural energy measures (LTEM) are added in the feature bank (263+108=371). An individual class accuracy of 91.40% is obtained for MENI (class-1), 91.43% for AST (class-2), 94.29% for NORM (class-3) and an overall classification accuracy of 92.43% is achieved. The results are calculated with and without addition of LTEM feature with Principle component analysis (PCA)-ANN. LTEM-PCA-ANN approach improved results with an overall accuracy of 93.34%. The texture patterns obtained were clear enough to differentiate between MENI (class-1) and AST (class-2) despite of necrotic and cystic component and location and size of tumor. LTEM detected fundamental texture properties such as level, edge, spot, wave and ripple in both horizontal and vertical directions which boosted the texture energy.

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Section: Background Theorymentioning

confidence: 99%

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Tiwari¹,

Sachdeva²,

Ahuja³

et al. 2017

IJCIS

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“…Finally, computer-aided diagnostic techniques, yet used in some screening programs [151,152] , could be added to standard EUS images for the differentiation of pancreatic carcinoma from chronic pancreatitis [151,153] . With digital image processing and computer-aided EUS image differentiation technologies, physicians could use the computer output as a ''second opinion'' and make the final decisions as reported by the high diagnostic accuracy (98%) of a recent study [154] .…”

Section: Future Perspectivesmentioning

confidence: 99%

Endoscopic ultrasonography for surveillance of individuals at high risk for pancreatic cancer

Lami

Biagini

Galli

2014

WJGE

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Pancreatic cancer is a highly lethal disease with a genetic susceptibility and familial aggregation found in 3%-16% of patients. Early diagnosis remains the only hope for curative treatment and improvement of prognosis. This can be reached by the implementation of an intensive screening program, actually recommended for individuals at high-risk for pancreatic cancer development. The aim of this strategy is to identify premalignant precursors or asymptomatic pancreatic cancer lesions, curable by surgery. Endoscopic ultrasound (EUS) with or without fine needle aspiration (FNA) seems to be the most promising technique for early detection of pancreatic cancer. It has been described as a highly sensitive and accurate tool, especially for small and cystic lesions. Pancreatic intraepithelial neoplasia, a precursor lesion which is highly represented in highrisk individuals, seems to have characteristics chronic pancreatitis-like changes well detected by EUS. Many screening protocols have demonstrated high diagnostic yields for pancreatic pre-malignant lesions, allowing prophylactic pancreatectomies. However, it shows a high interobserver variety even among experienced endosonographers and a low sensitivity in case of chronic pancreatitis. Some new techniques such as contrast-enhanced harmonic EUS, computer-aided diagnostic techniques, confocal laser endomicroscopy miniprobe and the detection of DNA abnormalities or protein markers by FNA, promise improvement of the diagnostic yield of EUS. As the resolution of imaging improves and as our knowledge of precursor lesions grows, we believe that EUS could become the most suitable method to detect curable pancreatic neoplasms in correctly identified asymptomatic at-risk patients.© 2014 Baishideng Publishing Group Inc. All rights reserved. Key words: Endoscopic ultrasonography; Pancreatic cancer; SurveillanceCore tip: In the era of early diagnosis and screening programs, endoscopic ultrasound (EUS) represents the most promising tool able to identify pancreatic precursor neoplasms in high risk individuals. If compared to other imaging techniques, it is highly accurate to diagnose small pancreatic cancer and pre-malignant lesions, with very low rate of complications and limitations. Here are reported the current role of EUS in various international screening programs and its future possible developments.

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“…In recent years, various kinds of CAD methods have been developed in the neuroradiology using MR images [25][26][27][28][29][30][31]. Radiologists expect that CAD systems can improve their diagnostic abilities based on synergistic effects between the computer's and radiologist's high abilities by using the information analysis including the medical images.…”

Section: Open Accessmentioning

confidence: 99%

“…These relationships back up the assumption derived from our observation in Figure 6. Many image features have been designed for characterizing specific lesions or diseases in the neuroradiological CAD system [25][26][27][28][29][30][31]41].…”

Section: Image Feature Extractionmentioning

confidence: 99%

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

et al. 2009

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This paper reviews the basics and recent researches of computer-aided diagnosis (CAD) systems for assisting neuroradiologists in detection of brain diseases, e.g., asymptomatic unruptured aneurysms, Alzheimer's disease, vascular dementia, and multiple sclerosis (MS), in magnetic resonance (MR) images. The CAD systems consist of image feature extraction based on image processing techniques and machine learning classifiers such as linear discriminant analysis, artificial neural networks, and support vector machines. We introduce useful examples of the CAD systems in the neuroradiology, and conclude with possibilities in the future of the CAD systems for brain diseases in MR images.

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Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs

Cited by 95 publications

References 22 publications

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Endoscopic ultrasonography for surveillance of individuals at high risk for pancreatic cancer

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

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