scholarly journals Skin Cancer Analysis with Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 2663-2672
Author(s):  
Siddharth Raj Dash
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Skin Cancer ◽  
Medical Diagnosis ◽  
Skin Diseases ◽  
Detection System ◽  
Technological Advancement ◽  
Image Processing Techniques ◽  
The Cost ◽  
Processing Techniques

Skin diseases are some of the most common diseases and are often difficult to diagnose than other diseases. Skin diseases may be caused by fungus, bacteria, allergic reaction, viruses, cancer etc. The technological advancement in laser diagnosis and Photonics based medical diagnosis has made it possible to diagnose the skin diseases much more quickly and accurately. But the cost of diagnostics is time-consuming and very expensive. Hence, we can use image processing techniques to help build automated preliminary detection system for such dermatological diagnostics.

Detecting Thread Features Based on Digital Image Processing Techniques

2014 ◽  
Vol 889-890 ◽  
pp. 1107-1110
Author(s):  
Han Ming Cai ◽  
Pei Yao Wang ◽  
Xiao Mei Song
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Measurement Accuracy ◽  
Measuring Method ◽  
Image Processing Techniques ◽  
Basic Image ◽  
The Cost ◽  
Processing Techniques ◽  
Gauge Measurement

Thread features of the traditional measuring method mainly adopts working gauge measurement, due to limitations in the traditional thread features measurement accuracy is relatively low, the efficiency is low, the cost is high. The thread features detection method based on digital image processing techniques using CCD to obtain basic image of thread, processing the thread image, extracting thread outline, calculating thread features through the computer, improves the efficiency, saves the cost.

Automatic lung nodule detection system using image processing techniques in computed tomography

2020 ◽  
Vol 56 ◽  
pp. 101659 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Chang-Chiun Huang ◽  
Jing-Jhong Siao ◽  
Chia-Wen Hsieh ◽  
Vu Quang Huy ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Detection System ◽  
Lung Nodule ◽  
Image Processing Techniques ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Processing Techniques

scholarly journals Lung Cancer Detection using Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 3256-3262 ◽  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Detection System ◽  
Histogram Equalization ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Cad System ◽  
Processing Techniques

The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy

Detection of Blood-Related Diseases Using Deep Neural Nets

2019 ◽  
pp. 1-15
Author(s):  
Rajithkumar B. K. ◽  
Shilpa D. R. ◽  
Uma B. V.
Keyword(s):  
Image Processing ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Neural Nets ◽  
Laboratory Methods ◽  
Intelligent Algorithms ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Traditional Image

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

scholarly journals Specifics of Using Image Processing Techniques for Blood Smear Analysis

2020 ◽  
Vol 1 (6) ◽  
pp. 1-6
Author(s):  
Vyacheslav Lyashenko ◽  
Tetiana Sinelnikova ◽  
Oleksandr Zeleniy ◽  
Asaad Mohammed Ahmed Babker
Keyword(s):  
Image Processing ◽  
Human Health ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Processing Techniques ◽  
Important Stage ◽  
Blood Smears ◽  
Processing Techniques

The process of medical diagnosis is an important stage in the study of human health. One of the directions of such diagnostics is the analysis of images of blood smears. In doing so, it is important to use different methods and analysis tools for image processing. It is also important to consider the specificity of blood smear imaging. The paper discusses various methods for analyzing blood smear images. The features of the application of the image processing technique for the analysis of a blood smear are highlighted. The results of processing blood smear images are presented.

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