با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Simultaneous feature weighting and parameter determination of Neural Networks using Ant Lion Optimization for the classification of breast cancer
وزن همزمان ویژگی ها و تعیین پارامتر شبکه های عصبی با استفاده از بهینه سازی مورچه ها برای طبقه بندی سرطان پستان-2020
In this paper, feature weighting is used to develop an effective computer-aided diagnosis system for breast cancer. Feature weighting is employed because it boosts the classification performance more as compared to feature subset selection. Specifically, a wrapper method utilizing the Ant Lion Optimization algorithm is presented that searches for best feature weights and parametric values of Multilayer Neural Network simultaneously. The selection of hidden neurons and backpropagation training algorithms are used as parameters of neural networks. The performance of the proposed approach is evaluated on three breast cancer datasets. The data is initially normalized using tanh method to remove the effects of dominant features and outliers. The results show that the proposed wrapper method has a better ability to attain higher accuracy as compared to the existing techniques. The obtained high classification performance validates the work which has the potential for becoming an alternative to the other well-known techniques.
Keywords: Antlion optimization | Breast cancer | Feature weighting | Neural Networks
A systematic survey of computer-aided diagnosis in medicine: Past and present developments
مرور سیستماتیک تشخیص کمک به رایانه در پزشکی: تحولات گذشته و حال-2019
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diag- nostic decision-making process of medical experts, they can be considered as expert systems in medicine. Furthermore, CAD systems in medicine may process clinical data that can be complex and/or massive in size. They do so in order to infer new knowledge from data and use that knowledge to improve their diagnostic performance over time. Therefore, such systems can also be viewed as intelligent systems be- cause they use a feedback mechanism to improve their performance over time. The main aim of the literature survey described in this paper is to provide a comprehensive overview of past and current CAD developments. This survey/review can be of significant value to researchers and professionals in medicine and computer science. There are already some reviews about specific aspects of CAD in medicine. How- ever, this paper focuses on the entire spectrum of the capabilities of CAD systems in medicine. It also identifies the key developments that have led to today’s state-of-the-art in this area. It presents an ex- tensive and systematic literature review of CAD in medicine, based on 251 carefully selected publica- tions. While medicine and computer science have advanced dramatically in recent years, each area has also become profoundly more complex. This paper advocates that in order to further develop and im- prove CAD, it is required to have well-coordinated work among researchers and professionals in these two constituent fields. Finally, this survey helps to highlight areas where there are opportunities to make significant new contributions. This may profoundly impact future research in medicine and in select areas of computer science.
Keywords: Computer-aided diagnosis | Computer-aided detection | Expert and intelligent systems | Computerized signal analysis | Segmentation | Classification
Medical image classification using synergic deep learning
طبقه بندی تصویر پزشکی با استفاده از یادگیری عمیق هم افزایی-2019
The classification of medical images is an essential task in computer-aided diagnosis, medical image re- trieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains challenging due to the significant intra-class variation and inter-class similarity caused by the diversity of imaging modalities and clinical pathologies. In this paper, we propose a synergic deep learning (SDL) model to address this issue by using multiple deep convo- lutional neural networks (DCNNs) simultaneously and enabling them to mutually learn from each other. Each pair of DCNNs has their learned image representation concatenated as the input of a synergic net- work, which has a fully connected structure that predicts whether the pair of input images belong to the same class. Thus, if one DCNN makes a correct classification, a mistake made by the other DCNN leads to a synergic error that serves as an extra force to update the model. This model can be trained end-to-end under the supervision of classification errors from DCNNs and synergic errors from each pair of DCNNs. Our experimental results on the ImageCLEF-2015, ImageCLEF-2016, ISIC-2016, and ISIC-2017 datasets in- dicate that the proposed SDL model achieves the state-of-the-art performance in these medical image classification tasks.
Keywords: Medical image classification | Intra-class variation | Inter-class similarity | Synergic deep learning model
Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things
یادگیری تقویتی عمیق با کاربرد آن برای تشخیص سرطان ریه در اینترنت اشیاء پزشکی -2019
Recently, deep reinforcement learning has achieved great success by integrating deep learning models into reinforcement learning algorithms in various applications such as computer games and robots. Specially, it is promising for computer-aided diagnosis and treatment to combine deep reinforcement learning with medical big data generated and collected from medical Internet of Things. In this paper, we focus on the potential of the deep reinforcement learning for lung cancer detection as many people are suffering from the lung tumor and about 1.8 million patients died from lung cancer in 2018. Early detection and diagnosis of lung tumor can significantly improve the treatment effect and prolong survival. In this work, we present several representative deep reinforcement learning models that are potential to use for lung cancer detection. Furthermore, we summarize the common types of lung cancer and the main characteristics of each type. Finally, we point out the open challenges and possible future research directions of applying deep reinforcement learning to lung cancer detection, which is expected to promote the evolution of smart medicine with medical Internet of Things.
Keywords: Smart medicine | Medical Internet of Things | Deep reinforcement learning | Lung cancer
Multi-Class Multi-Level Classification Algorithm for Skin Lesions Classification using Machine Learning Techniques
الگوریتم طبقه بندی چند مرحله ای چند سطح برای طبقه بندی ضایعات پوستی با استفاده از تکنیک های یادگیری ماشین-2019
Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the “divide and conquer” rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions.
Keywords: skin lesion classification | computer-aided diagnosis | machine learning | deep learning | texture & colour features | melanoma classification | eczema classification
Computer-aided diagnosis: A survey with bibliometric analysis
تشخیص کمک-کامپیوتر : یک بررسی با تجزیه و تحلیل کتابشناختی-2017
Article history:Received 27 August 2016Received in revised form 28 January 2017 Accepted 4 February 2017Keywords:Computer-aided diagnosis CADCitation network analysis Bibliometric analysisComputer-aided diagnosis (CAD) has been a promising area of research over the last two decades. How- ever, CAD is a very complicated subject because it involves a number of medicine and engineering-related ﬁelds. To develop a research overview of CAD, we conducted a literature survey with bibliometric anal- ysis, which we report here. Our study determined that CAD research has been classiﬁed and categorized according to disease type and imaging modality. This classiﬁcation began with the CAD of mammograms and eventually progressed to that of brain disease. Furthermore, based on our results, we discuss future directions and opportunities for CAD research. First, in contrast to the typical hypothetical approach, the data-driven approach has shown promise. Second, the normalization of the test datasets and an evalu- ation method is necessary when adopting an algorithm and a system. Third, we discuss opportunities for the co-evolution of CAD research and imaging instruments—for example, the CAD of bones and pan- creatic cancer. Fourth, the potential of synergy with CAD and clinical decision support systems is also discussed.© 2017 Elsevier B.V. All rights reserved.
Keywords:Computer-aided diagnosis | CAD | Citation network analysis | Bibliometric analysis
A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis
ابزار اتوماتیک شده پیشگیری ا از رتینوپاتی دیابتی بر اساس تجزیه و تحلیل کامپیوتری تصویر شبکیه-2017
Aim: This paper presents a methodology and first results of an automatic detection system of first signs of Diabetic Retinopathy (DR) in fundus images, developed for the Health Ministry of the Andalusian Regional Government (Spain). Material and methods: The system detects the presence of microaneurysms and haemorrhages in retinography by means of techniques of digital image processing and supervised classification. Evaluation was conducted on 1058 images of 529 diabetic patients at risk of presenting evidence of DR (an image of each eye is provided). To this end, a ground-truth diagnosis was created based on gradations performed by 3 independent ophthalmology specialists. Results: The comparison between the diagnosis provided by the system and the reference clinical diagnosis shows that the system can work at a level of sensitivity that is similar to that achieved by experts (0.9380 sensitivity per patient against 0.9416 sensitivity of several specialists). False negatives have proven to be mild cases. Moreover, while the specificity of the system is significantly lower than that of human graders (0.5098), it is high enough to screen more than half of the patients unaffected by the disease. Conclusion: Results are promising in integrating this system in DR screening programmes. At an early stage, the system could act as a pre-screening system, by screening healthy patients (with no obvious signs of DR) and identifying only those presenting signs of the disease.
Keywords:Diabetic retinopathy | Early detection system | Automated screening | Retinal image processing | Computer-aided diagnosis
Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach
تشخیص کامپیوتری توده های ماموگرافی براساس روش بازیابی تصویر مبتنی بر محتوا-2017
Article history:Received 1 September 2016Revised 28 April 2017Accepted 25 May 2017Available online 26 May 2017Keywords: Mammography MassesCBIR CADx SVMIn this work, the incorporation of content-based image retrieval (CBIR) into computer aided diagnosis (CADx) is investigated, in order to contribute to the decision-making process of radiologists in the char- acterization of mammographic masses. The proposed scheme comprises two stages: A margin-speciﬁc su- pervised CBIR stage that retrieves images from reference cases along with a decision stage that is based on the retrieved items. The feature set utilized exploits state-of-the-art features along with a newly pro- posed texture descriptor, namely mHOG, targeted to capturing margin and core speciﬁc mass properties. Performance evaluation considers the CBIR and diagnosis stages separately and is addressed by using standard measures on an enhanced version of the widely adopted digital database for screening mam- mography (DDSM). The proposed scheme achieved improved performance of CADx of masses in X-ray mammography experimentally compared to the state-of-the-art.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Mammography | Masses | CBIR | CADx | SVM
Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography
تشخیص کامپیوتری از کیست پرایپیکال و تومور ادنتوژنیک کراتوسیستیک بر روی توموگرافی کامپیوتری پرتو مخروطی-2017
Article history:Received 2 May 2016Revised 15 April 2017Accepted 26 May 2017Keywords:Computer aided diagnosis Dental apical lesion ClassiﬁerCone beam computed tomography Periapical cyst and keratocystic odontogenic tumorVolumetric textural features Dental image datasetBackground and objectives: In this article, we propose a decision support system for effective classiﬁ- cation of dental periapical cyst and keratocystic odontogenic tumor (KCOT) lesions obtained via cone beam computed tomography (CBCT). CBCT has been effectively used in recent years for diagnosing dental pathologies and determining their boundaries and content. Unlike other imaging techniques, CBCT pro- vides detailed and distinctive information about the pathologies by enabling a three-dimensional (3D) image of the region to be displayed.Methods: We employed 50 CBCT 3D image dataset ﬁles as the full dataset of our study. These datasets were identiﬁed by experts as periapical cyst and KCOT lesions according to the clinical, radiographic and histopathologic features. Segmentation operations were performed on the CBCT images using viewer soft- ware that we developed. Using the tools of this software, we marked the lesional volume of interest and calculated and applied the order statistics and 3D gray-level co-occurrence matrix for each CBCT dataset. A feature vector of the lesional region, including 636 different feature items, was created from those statistics. Six classiﬁers were used for the classiﬁcation experiments.Results: The Support Vector Machine (SVM) classiﬁer achieved the best classiﬁcation performance with 100% accuracy, and 100% F-score (F1) scores as a result of the experiments in which a ten-fold cross vali- dation method was used with a forward feature selection algorithm. SVM achieved the best classiﬁcation performance with 96.00% accuracy, and 96.00% F1 scores in the experiments in which a split sample val- idation method was used with a forward feature selection algorithm. SVM additionally achieved the best performance of 94.00% accuracy, and 93.88% F1 in which a leave-one-out (LOOCV) method was used with a forward feature selection algorithm.Conclusions: Based on the results, we determined that periapical cyst and KCOT lesions can be classiﬁed with a high accuracy with the models that we built using the new dataset selected for this study. The studies mentioned in this article, along with the selected 3D dataset, 3D statistics calculated from the dataset, and performance results of the different classiﬁers, comprise an important contribution to the ﬁeld of computer-aided diagnosis of dental apical lesions.© 2017 Elsevier B.V. All rights reserved.
Keywords: Computer aided diagnosis | Dental apical lesion | Classifier | Cone beam computed tomography | Periapical cyst and keratocystic odontogenic | tumor | Volumetric textural features | Dental image dataset
Exploring the color feature power for psoriasis risk stratification and classification_ A data mining paradigm
Exploring the color feature power for psoriasis risk stratification and classification_ A data mining paradigm-2015
A large percentage of dermatologist's decision in psoriasis disease assessment is based on color. The current computer-aided diagnosis systems for psoriasis risk stratiﬁcation and classiﬁcation lack the vigor of color paradigm. The paper presents an automated psoriasis computer-aided diagnosis (pCAD) system for classiﬁcation of psoriasis skin images into psoriatic lesion and healthy skin, which solves the two major challenges: (i) fulﬁlls the color feature requirements and (ii) selects the powerful dominant color features while retaining high classiﬁcation accuracy.Fourteen color spaces are discovered for psoriasis disease analysis leading to 86 color features. The pCAD system is implemented in a support vector-based machine learning framework where the ofﬂine image data set is used for computing machine learning ofﬂine color machine learning parameters. These are then used for transformation of the online color features to predict the class labels for healthy vs. diseased cases. The above paradigm uses principal component analysis for color feature selection of dominant features, keeping the original color feature unaltered. Using the cross-validation protocol, the above machine learning protocol is compared against the standalone grayscale features with 60 features and against the combined grayscale and color feature set of 146.Using a ﬁxed data size of 540 images with equal number of healthy and diseased, 10 fold cross- validation protocol, and SVM of polynomial kernel of type two, pCAD system shows an accuracy of 99.94% with sensitivity and speciﬁcity of 99.93% and 99.96%. Using a varying data size protocol, the mean classiﬁcation accuracies for color, grayscale, and combined scenarios are: 92.85%, 93.83% and 93.99%, respectively. The reliability of the system in these three scenarios are: 94.42%, 97.39% and 96.00%, respectively. We conclude that pCAD system using color space alone is compatible to grayscale space or combined color and grayscale spaces. We validated our pCAD system against facial color databases and the results are consistent in accuracy and reliability.& 2015 Elsevier Ltd. All rights reserved.
Keywords: Psoriasis | Color space | PCA | Classification | Feature power | Reliability