دانلود و نمایش مقالات مرتبط با Cancer detection::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - Cancer detection

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 A grid-quadtree model selection method for support vector machines
روش انتخاب مدل شبکه چهارگوش برای ماشینهای بردار پشتیبانی-2020
In this paper, a new model selection approach for Support Vector Machine (SVM), which integrates the quadtree technique with the grid search, denominated grid-quadtree (GQ) is proposed. The developed method is the first in the literature to apply the quadtree for the SVM parameters optimization. The SVM is a machine-learning technique for pattern recognition whose performance relies on its parameters determination. Thus, the model selection problem for SVM is an important field of study and requires expert and intelligent systems to solve it. Real classification data sets involve a huge number of instances and features, and the greater is the training data set dimension, the larger is the cost of a recognition system. The grid search (GS) is the most popular and the simplest method to select parameters for SVM. However, it is time-consuming, which limits its application for big-sized problems. With this in mind, the main idea of this research is to apply the quadtree technique to the GS to make it faster. Hence, this may lower computational time cost for solving problems such as bio-identification, bank credit risk and cancer detection. Based on the asymptotic behaviors of the SVM, it was noticeably observed that the quadtree is able to avoid the GS full search space evaluation. As a consequence, the GQ carries out fewer parameters analysis, solving the same problem with much more efficiency. To assess the GQ performance, ten classification benchmark data set were used. The obtained results were compared with the ones of the traditional GS. The outcomes showed that the GQ is able to find parameters that are as good as the GS ones, executing 78.8124% to 85.8415% fewer operations. This research points out that the adoption of quadtree expressively reduces the computational time of the original GS, making it much more efficient to deal with high dimensional and large data sets.
Keywords: Support vector machine | Parameter determination | Quadtree | Grid search
مقاله انگلیسی
2 Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope
تشخیص سرطان پوست با الگوریتم های یادگیری عمیق و آنالیز صدا: یک مطالعه بالینی آینده نگر از یک درموسکوپ ابتدایی-2019
Background: Skin cancer (SC), especiallymelanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was demonstrated that diagnostics by dermoscopy are improved by applying an additional sonification (data to sound waves conversion) layer on DL algorithms. The aim of the study was to determine the impact of image quality on accuracy of diagnosis by sonification employing a rudimentary skin magnifier with polarized light (SMP). Methods: Dermoscopy images acquired by SMP were processed by a first deep learning algorithm and sonified. Audio output was further analyzed by a different secondary DL. Study criteria outcomes of SMP were specificity and sensitivity,which were further processed by a F2-score, i.e. applying a twice extra weight to sensitivity over positive predictive values. Findings: Patients (n=73) fulfilling inclusion criteriawere referred to biopsy. SMP analysis metrics resulted in a receiver operator characteristic curve AUCs of 0.814 (95% CI, 0.798–0.831). SMP achieved a F2-score sensitivity of 91.7%, specificity of 41.8% and positive predictive value of 57.3%.Diagnosing the sameset of patients lesions by an advanced dermoscope resulted in a F2-score sensitivity of 89.5%, specificity of 57.8% and a positive predictive value of 59.9% (P=NS). Interpretation: DL processing of dermoscopic images followed by sonification results in an accurate diagnostic output for SMP, implying that the quality of the dermoscope is not the major factor influencing DL diagnosis of skin cancer. Present system might assist all healthcare providers as a feasible computer-assisted detection system. Fund: Bostel Technologies.
Keywords: Skin cancer | Deep learning | Dermoscopy | Sonification | Melanoma | Telemedicine | Artificial intelligence
مقاله انگلیسی
3 A comparative study of deep learning architectures on melanoma detection
مطالعه تطبیقی معماریهای یادگیری عمیق در تشخیص ملانوما-2019
Melanoma is the most aggressive type of skin cancer, which significantly reduces the life expectancy. Early detection of melanoma can reduce the morbidity and mortality associated with skin cancer. Dermoscopic images acquired by dermoscopic instruments are used in computational analysis for skin cancer detection. However, some image quality limitations such as noises, shadows, artefacts exist that could compromise the robustness of the skin image analysis. Hence, developing an automatic intelligent system for skin cancer diagnosis with accurate detection rate is crucial. In this paper, we evaluate the performance of several state-of-the-art convolutional neural networks in dermoscopic images of skin lesions. Our experiment is conducted on a graphics processing unit (GPU) to speed up the training and deployment process. To enhance the quality of images, we employ different pre-processing steps. We also apply data augmentation methodology such as horizontal and vertical flipping techniques to address the class skewness problem. Both pre-processing and data augmentation could help to improve the final accuracy.
Keywords: Cancer classification | Computational diagnosis | Convolutional neural networks | Deep learning | Melanoma detection
مقاله انگلیسی
4 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
مقاله انگلیسی
5 Feature selection based on closed frequent itemset mining: A case study on SAGE data classification
انتخاب ویژگی بر اساس کاوش مجموعه اقلام مکرر مسدود: مطالعه موردی در طبقه بندی داده های SAGE-2015
Cancer is curable if it can be detected early. One way to detect cancer is by analyzing the change in expression of genes in the suspected tissue. Serial analysis of gene expression (SAGE) is a sequencing technique used for measuring the expression levels of genes. Cancer detection problem can be posed as binary classification problem like whether a tissue is cancerous or normal. SAGE libraries contain expression levels of thousands of genes which are the features. It is impossible to consider all these features for classification and also the general feature selection algorithms are not efficient with this data. In this paper, closed frequent itemset mining is proposed as a feature selection technique for identifying a small set of features which can distinguish the two classes efficiently. The performance of the proposed technique is evaluated on SAGE data related to breast tissue and a group of 26 genes are selected as best features. Two well known classifiers, extreme learning machine (ELM) and support vector machine (SVM), are used to evaluate the effectiveness of the selected features in classification and found that the proposed method works well with these classifiers.& 2014 Elsevier B.V. All rights reserved.
Keywords: Closed frequent itemset mining | Feature selection | Serial analysis of gene expression | Extreme learning machine | Support vector machine | Classification
مقاله انگلیسی
6 نظارت بر وضعیت سلامتی با استفاده از هوای بازدمی با بینی الکترونیکی / ماشین بویایی
سال انتشار: 2012 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 18
در یک جامعه سالخورده ، مردم توجه خود را بیش از پیش به ارزیابیهای روزمره وضعیت سلامتی خود معطوف میکنند . انها علاوه بر اینکه وضعیت سلامتی خود را چک میکنند ، از ازمایشهای پزشکان نیز استفاده میکنند ، همچنین ابزار زیست پزشکی نیز در این راستا وجود دارد که قابلیت نظارت و نشان دادن وضعیت بهداشت و سلامت را نیز دارا میباشد . در این مقاله ، ما سیستم بینی الکترونیکی را پیشنهاد میدهیم که برای نشان دادن وضعیت سلامت افراد و تنفس آنها توسعه یافته است . متال – پورفیرین ها (MPs) / SWNT –COOH و پلیمر / SWNT-COOH از جمله سنسورهای نانوکامپوزیت هستند که بعنوان مجموعه ایی از سنسورهای گازی شیمیایی در کنارِ سیستم بینی الکترونیکی مورد استفاده قرار میگیرند . این مواد حساس نسبت به مولکولهای بوی موجود در هوای بازدمی حساس هستند . ابزار مورد استفاده توان کمتری نیاز دارند و میتوان در دمای اتاق نیز از آنها استفاده نمود . یک ازمایش مقدماتی روی گروه نمونه شامل بیماران مبتلا به سرطان و داوطلبان سالم انجام شد تا بتوان وضعیت سلامتی آنها را با هم مقایسه نمود . در این ازمایش مشخص شد که بینی الکترونیکی میتواند هوای بازدمی را تشخیص دهد و الگوی بوی هوای بازدم / بوی دهان را برای هر فرد نشان دهد . این دستگاه میتواند خطر ابتلا به بیماریهای عفونی و سایر بیماریها را کاهش دهد .
راهنمای موضوعی : تشخیص سرطان | بینی الکترونیکی | هوای بازدمی | سنسور گازی | PCA | ترکیبات آلی فرار (VOCs) .
مقاله ترجمه شده
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 4656 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 4656 :::::::: افراد آنلاین: 78