Early Esophageal Cancer detection using Deep learning Techniques. (Review Article)

ObjectiveTo develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images.MethodsWe retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared.ResultsThe sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively.ConclusionsDeep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

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A Comparative Analysis on Deep Learning Techniques for Skin Cancer Detection and Skin Lesion Segmentation

2021 International Conference on Communication, Control and Information Sciences (ICCISc) ◽

10.1109/iccisc52257.2021.9484873 ◽

2021 ◽

Author(s):

Saurav Vinod ◽

Manoj V. Thomas

Keyword(s):

Deep Learning ◽

Comparative Analysis ◽

Skin Cancer ◽

Skin Lesion ◽

Cancer Detection ◽

Lesion Segmentation ◽

Learning Techniques ◽

Skin Cancer Detection

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

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012048 ◽

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.

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Melanoma and Breast Cancer Detection using Deep Learning Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2019.10107 ◽

2019 ◽

Vol 7 (10) ◽

pp. 702-703

Author(s):

Miss. Ashmita Dutta

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Cancer Detection ◽

Breast Cancer Detection ◽

Learning Techniques

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A Review on Prostate Cancer Detection using Deep Learning Techniques

Journal of Computational Science and Intelligent Technologies ◽

10.53409/mnaa.jcsit20201204 ◽

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.

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