10,098 research outputs found
Lung Cancer Detection Using Artificial Neural Network
In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy
ANN for Lung Cancer Detection
In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
Pneumonia is a life-threatening disease, which occurs in the lungs caused by
either bacterial or viral infection. It can be life-endangering if not acted
upon in the right time and thus an early diagnosis of pneumonia is vital. The
aim of this paper is to automatically detect bacterial and viral pneumonia
using digital x-ray images. It provides a detailed report on advances made in
making accurate detection of pneumonia and then presents the methodology
adopted by the authors. Four different pre-trained deep Convolutional Neural
Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for
transfer learning. 5247 Bacterial, viral and normal chest x-rays images
underwent preprocessing techniques and the modified images were trained for the
transfer learning based classification task. In this work, the authors have
reported three schemes of classifications: normal vs pneumonia, bacterial vs
viral pneumonia and normal, bacterial and viral pneumonia. The classification
accuracy of normal and pneumonia images, bacterial and viral pneumonia images,
and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3%
respectively. This is the highest accuracy in any scheme than the accuracies
reported in the literature. Therefore, the proposed study can be useful in
faster-diagnosing pneumonia by the radiologist and can help in the fast airport
screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with
arXiv:2003.1314
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Using Topological Data Analysis for diagnosis pulmonary embolism
Pulmonary Embolism (PE) is a common and potentially lethal condition. Most
patients die within the first few hours from the event. Despite diagnostic
advances, delays and underdiagnosis in PE are common.To increase the diagnostic
performance in PE, current diagnostic work-up of patients with suspected acute
pulmonary embolism usually starts with the assessment of clinical pretest
probability using plasma d-Dimer measurement and clinical prediction rules. The
most validated and widely used clinical decision rules are the Wells and Geneva
Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE
based on topological data analysis and artificial neural network. Filter or
wrapper methods for features reduction cannot be applied to our dataset: the
application of these algorithms can only be performed on datasets without
missing data. Instead, we applied Topological data analysis (TDA) to overcome
the hurdle of processing datasets with null values missing data. A topological
network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE
patient topology identified two ares in the pathological group and hence two
distinct clusters of PE patient populations. Additionally, the topological
netowrk detected several sub-groups among healthy patients that likely are
affected with non-PE diseases. TDA was further utilized to identify key
features which are best associated as diagnostic factors for PE and used this
information to define the input space for a back-propagation artificial neural
network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is
greater than the AUCs of the scores (Wells and revised Geneva) used among
physicians. The results demonstrate topological data analysis and the BP-ANN,
when used in combination, can produce better predictive models than Wells or
revised Geneva scores system for the analyzed cohortComment: 18 pages, 5 figures, 6 tables. arXiv admin note: text overlap with
arXiv:cs/0308031 by other authors without attributio
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