Enhancing in vivo nose cancer detection with rapid fiberoptic Raman and deep learning techniques (Conference Presentation)

2020 ◽  
Author(s):  
Chi Shu ◽  
Hanshu Yan ◽  
Kan Lin ◽  
Chwee Ming Lim ◽  
Wei Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Detection ◽  
Learning Techniques

scholarly journals A Medical Service Application Based on 3D-CNN and Knowledge Graph

2021 ◽  
Vol 2078 (1) ◽  
pp. 012048
Author(s):  
Jiasheng Ni
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Knowledge Graph ◽  
Medical Response ◽  
Learning Techniques ◽  
3D Cnn ◽  
Lung Cancer Detection

Abstract Remote medical prognosis application is a category of medical tests tool designed to collect patients’ body conditions and offer diagnosis results synchronously. However, most online applications are predicated on a simple chat bot which typically redirect patients to other online medical websites, which undermines the user experience and may prompt useless information for their reference. To tackle these issues, this paper proposed a medical prognosis application with deep learning techniques for a more responsive and intelligent medical prognosis procedure. This application can be break down into three parts-lung cancer detection, a database-supporting medical QA bot and a Hierarchical Bidirectional LSTM model (HBDA). A 3D-CNN model is built for the lung cancer detection, with a sequence of sliced CT images as inputs and outputs a probability scaler for tumor indications. A knowledge graph is applied in the medical QA bot implementation and the HBDA model is designed for semantic segmentation in order to better capture users’ intention in medical questions. For the performance of the medical prognosis, since we have limited computer memory, the 3D-CNN didn’t perform very well on detecting tumor conditions in the CT images with accuracy at around 70%. The knowledge graph-based medical QA bot intelligently recognize the underlying pattern in patients’ question and delivered decent medical response. The HBDA model performs well on distinguish the similarities and disparities between various medical questions, reaching accuracy at 90%. These results shed light for the feasibility of utilizing deep learning techniques such as 3D-CNN, Knowledge Graph, and Hierarchical Bi-directional LSTM to simulate the medical prognosis process.

scholarly journals A Review on Prostate Cancer Detection using Deep Learning Techniques

2020 ◽  
Vol 1 (2) ◽  
pp. 26-33
Author(s):  
Narmatha C ◽  
◽  
Surendra Prasad M ◽  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Medical Image Analysis ◽  
Diagnostic Methods ◽  
Convolutional Networks ◽  
Prostate Segmentation ◽  
Learning Techniques ◽  
Imaging Methodology ◽  
Medical Effect

The second most diagnosed disease of men throughout the world is Prostate cancer (PCa). 28% of cancers in men result in the prostate, making PCa and its identification an essential focus in cancer research. Hence, developing effective diagnostic methods for PCa is very significant and has critical medical effect. These methods could improve the advantages of treatment and enhance the patients' survival chance. Imaging plays a significant role in the identification of PCa. Prostate segmentation and classification is a difficult process, and the difficulties fundamentally vary with one imaging methodology then onto the next. For segmentation and classification, deep learning algorithms, specifically convolutional networks, have quickly become an optional technique for medical image analysis. In this survey, various types of imaging modalities utilized for diagnosing PCa is reviewed and researches made on the detection of PCa is analyzed. Most of the researches are done in machine learning based and deep learning based techniques. Based on the results obtained from the analysis of these researches, deep learning based techniques plays a significant and promising part in detecting PCa. Most of the techniques are based on computer aided detection (CAD) systems, which follows preprocessing, segmentation, feature extraction, and classification processes, which yield efficient results in detecting PCa. As a conclusion from the analysis of some recent works, deep learning based techniques are adequate for the detection of PCa.

scholarly journals Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection

2020 ◽  
Author(s):  
Marcel Bengs ◽  
Stephan Westermann ◽  
Nils Gessert ◽  
Dennis Eggert ◽  
Andreas O. H. Gerstner ◽  
...  
Keyword(s):  
Deep Learning ◽  
Laryngeal Cancer ◽  
Cancer Detection ◽  
Learning Methods

USDL: Inexpensive Medical Imaging Using Deep Learning Techniques and Ultrasound Technology

2020 ◽  
Author(s):  
Manish Balamurugan ◽  
Kathryn Chung ◽  
Venkat Kuppoor ◽  
Smruti Mahapatra ◽  
Aliaksei Pustavoitau ◽  
...  
Keyword(s):  
Deep Learning ◽  
Similarity Index ◽  
Structural Similarity ◽  
Space Representation ◽  
Ultrasound Images ◽  
Learning Techniques ◽  
Latent Space ◽  
Unsupervised Neural Network ◽  
Ultrasound Technology

Abstract In this study, we present USDL, a novel model that employs deep learning algorithms in order to reconstruct and enhance corrupted ultrasound images. We utilize an unsupervised neural network called an autoencoder which works by compressing its input into a latent-space representation and then reconstructing the output from this representation. We trained our model on a dataset that compromises of 15,700 in vivo images of the neck, wrist, elbow, and knee vasculature and compared the quality of the images generated using the structural similarity index (SSIM) and peak to noise ratio (PSNR). In closely simulated conditions, the architecture exhibited an average reconstruction accuracy of 90% as indicated by our SSIM. Our study demonstrates that USDL outperforms state of the art image enhancement and reconstruction techniques in both image quality and computational complexity, while maintaining the architecture efficiency.

scholarly journals Breast Cancer Detection using Deep Learning Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 3959-3963
Author(s):  
Prof. M. S. Choudhari
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Survival Rate ◽  
Cancer Detection ◽  
Deep Neural Network ◽  
The United States ◽  
Breast Cancer Detection ◽  
Detection Techniques ◽  
Learning Techniques

Breast cancer is the most common form of cancer among women and the second most common cancer in the world (an estimated 1 152 161 new cases per year), trailing only lung cancer .The current approach to this disease involves early detection and treatment. This approach in the United States yields an 85% 10-year survival rate. Survival is directly related to stage at diagnosis, as can be seen by a 98% 10- year survival rate for patients with stages 0 and I disease compared with a 65% 10-year survival rate for patients with stage III disease. To improve survival in this disease, more patients need to be identified at an early stage.Therefore, we evaluated existing and emerging technologies used for breast cancer screening and detection to identify areas for potential improvement. The main criteria for a good screening test are accuracy, high sensitivity, ease of use, acceptability to the population being screened (with regard to discomfort and time), and low cost. We can begins by describing commonly used breast cancer detection techniques and then delves into emerging modalities. Several studies addressing breast cancer using Deep learning techniques. Many claim that their algorithms are faster, easier, or more accurate than others . This system is based on thermal image processing and Deep learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this was to optimize the learning algorithm. In this system , we applied the deep neural network technique to select the best features and perfect parameter values of the deep machine learning. The present study proves that deep neural network can automatically find the best model by combining feature preprocessing methods and classification algorithms.

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