scholarly journals Neural Network Based Filtering Method for Cancer Detection

2021 ◽  
Vol 15 (1) ◽  
pp. 163-169
Author(s):  
J. Jaya ◽  
A. Sasi ◽  
B. Paulchamy ◽  
K.J. Sabareesaan ◽  
Sivakumar Rajagopal ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cancer Detection ◽  
Human Body ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Linear Filters ◽  
Distribution Method ◽  
Filtering Method ◽  
Specific Distribution

Objective: The growth of anomalous cells in the human body in an uncontrolled manner is characterized as cancer. The detection of cancer is a multi-stage process in the clinical examination. Methods: It is mainly involved with the assistance of radiological imaging. The imaging technique is used to identify the spread of cancer in the human body. This imaging-based detection can be improved by incorporating the Image Processing methodologies. In image processing, the preprocessing is applied at the lower-level abstraction. It removes the unwanted noise pixel present in the image, which also distributes the pixel values based on the specific distribution method. Results: Neural Network is a learning and processing engine, which is mainly used to create cognitive intelligence in various domains. In this work, the Neural Network (NN) based filtering approach is developed to improve the preprocessing operation in the cancer detection process. Conclusion: The performance of the proposed filtering method is compared with the existing linear and non-linear filters in terms of Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

scholarly journals Image Processing Method on Radiographic Image of Piping using MATLAB: Enhancement and Detection Process

2019 ◽  
Vol 8 (12) ◽  
pp. 5557-5561
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Original Data ◽  
Edge Enhancement ◽  
Radiographic Image ◽  
Radiographic Images ◽  
Filtering Method ◽  
Full Reference ◽  
Opening And Closing

Image enhancement is a pre-processing process to enhance the quality and information content of original data. This paper investigates two methods of image augmentation that is deployed to remove noise and improve radiographic images. The first method is image filtering, which consists of smoothing, sharpening and edge enhancement (Sobel & Prewitt) operations. The filtering method emphasizes certain characteristics or eliminates other details. While the second method is morphological technique that utilizes the opening and closing operation, which employed to removed distorted noise and imperfection on the processed images. Each method and operation applied to the image is evaluated subjectively based on the enhance image quality. The image quality measured using MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) which is a full reference metrics. The image quality results are compared to give a wide picture on the performance of the enhanced images. The image processing operations accomplished by using MATLAB image processing toolbox

scholarly journals Analysis of Medical Image Resizing Using Bicubic Interpolation Algorithm

2021 ◽  
Vol 14 (1) ◽  
pp. 20
Author(s):  
Bambang Krismono Triwijoyo ◽  
Ahmat Adil
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Image Interpolation ◽  
Interpolation Algorithm ◽  
Image Size ◽  
Pixel Value ◽  
Bicubic Interpolation

Image interpolation is the most basic requirement for many image processing tasks such as medical image processing. Image interpolation is a technique used in resizing an image. To change the image size, each pixel in the new image must be remapped to a location in the old image to calculate the new pixel value. There are many algorithms available for determining the new pixel value, most of which involve some form of interpolation between the closest pixels in the old image. In this paper, we use the Bicubic interpolation algorithm to change the size of medical images from the Messidor dataset and then analyze it by measuring it using three parameters Mean Square Error (MSE), Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR), and compare the results with Bilinear and Nearest-neighbor algorithms. The results showed that the Bicubic algorithm is better than Bilinear and Nearest-neighbor and the larger the image dimensions are resized, the higher the degree of similarity to the original image, but the level of computation complexity also increases.

scholarly journals A Novel Image Processing Methodology for X-ray Image Compression and Enhancement

2022 ◽  
pp. 1-8
Author(s):  
Calvin Omind Munna
Keyword(s):  
Image Processing ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
X Ray ◽  
Squared Error ◽  
Structure Similarity Index ◽  
Structure Similarity ◽  
Clinical Pictures

Currently, there a growing demand of data produced and stored in clinical domains. Therefore, for effective dealings of massive sets of data, a fusion methodology needs to be analyzed by considering the algorithmic complexities. For effective minimization of the severance of image content, hence minimizing the capacity to store and communicate data in optimal forms, image processing methodology has to be involved. In that case, in this research, two compression methodologies: lossy compression and lossless compression were utilized for the purpose of compressing images, which maintains the quality of images. Also, a number of sophisticated approaches to enhance the quality of the fused images have been applied. The methodologies have been assessed and various fusion findings have been presented. Lastly, performance parameters were obtained and evaluated with respect to sophisticated approaches. Structure Similarity Index Metric (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) are the metrics, which were utilized for the sample clinical pictures. Critical analysis of the measurement parameters shows higher efficiency compared to numerous image processing methods. This research draws understanding to these approaches and enables scientists to choose effective methodologies of a particular application.

scholarly journals Perceptual Image Super Resolution Using Deep Learning and Super Resolution Convolution Neural Networks (SRCNN)

2020 ◽  
Author(s):  
Nagaraj P ◽  
Muthamilsudar K ◽  
Naga Nehanth S ◽  
Mohammed Shahid R ◽  
Sujith Kumar V
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Convolution Neural Network ◽  
Resolution Image ◽  
Squared Error ◽  
Image Super Resolution ◽  
The Mean ◽  
State Of Art

The main objective of Perceptual Image Super Resolution is to obtain a high resoluted image from a normal low resolution image. The task is very simple that we just want to make a Low firmness appearance into a extraordinary resolution image. To perform this task we have various methods like Classical Approach in which we try to maximize the mean squared error, evaluate by PSNR(Peak-Signal-to-Noise-Ratio). The first method used to perform this operation was SRCNN (Super Resolution Convolution Neural Network) and these days many of them use DRCN and VDSR which are slightly upgraded methods. Another technique used for the purpose of upscaling to get a high resoluted image from normal little resolution image is the state of art by PSNR. This method was a quite simple one in which we take a low determination image as input and place in a convolution neural network(CNN) and produce a high resolution image as the output. In this technique the edges will be clearly defined, but the whole image will be blurred. This method is unable to produce good-looking textures.

scholarly journals Inception-V3 Architecture in Dermatoglyphics-Based Temperament Classification

2020 ◽  
Vol 3 (2) ◽  
pp. 173-174
Author(s):  
Mary Gift D. Dionson ◽  
El Jireh P. Bibangco
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Computer Science ◽  
Neural Network Model ◽  
Human Body ◽  
Fingerprint Analysis ◽  
Behavioral Psychology ◽  
Personality Classification

Personality classification is one of the areas of behavioral psychology that focuses on categorizing individuals. Different factors constitute the main currents of human personality. These factors turned out to be complicated and sometimes yield a biased result. Meanwhile, the entire human body reflects the character of its possessor more accurately than any set of questionnaires. Dermatoglyphics is the scientific study of fingerprints. Fingerprint patterns and ridge density are the viable bases in the classification of the personality of an individual. This uniqueness has expanded through research confirming parents' ability to identify their children's unique potentials through fingerprint analysis. Bridging the gap between computer science and psychology is one of the biggest challenges of the study. Exploring the possibilities revolves around image processing, where fingerprints served as image input and a deep learning convolutional neural network model implemented in the Inception-v3 architecture is used to analyze and classify different fingerprint patterns finally associate with the classified prints to its corresponding temperament type.

scholarly journals Automatic Black & White Images colorization using Convolutional neural network

2019 ◽  
Vol 2 (3) ◽  
pp. 1189-1195
Author(s):  
Omar Abdulwahhab Othman ◽  
Sait Ali Uymaz ◽  
Betül Uzbaş
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Signal To Noise Ratio ◽  
Color Space ◽  
Black And White ◽  
Deep Learning Algorithm ◽  
Squared Error ◽  
Lab Color Space

In this paper, automatic black and white image colorization method has been proposed. The study is based on the best-known deep learning algorithm CNN (Convolutional neural network). The Model that developed taking the input in gray scale and predict the color of image based on the dataset that trained on it. The color space used in this work is Lab Color space the model takes the L channel as the input and the ab channels as the output. The Image Net dataset used and random selected image have been used to construct a mini dataset of images that contains 39,604 images splitted into 80% training and 20% testing. The proposed method has been tested and evaluated on samples images with Mean-squared error and peak signal to noise ratio and reached an average of MSE= 51.36 and PSNR= 31.

scholarly journals Diagnosis of Malaria from Peripheral Blood Smear Images using Convolutional Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 12461-12464
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
General Population ◽  
Human Body ◽  
Blood Smear ◽  
Peripheral Blood Smear ◽  
Malaria Vectors ◽  
Deadly Disease

Malaria is a deadly disease brought about by Plasmodium parasites which affects the general population through the bites of female mosquitoes, called "malaria vectors." There are about five parasites species that cause malaria in human body, and two of the species namely P. falciparum , P.vivax pose the greatest threat. The most prominent technique to detect malaria is by taking blood smear samples to check if the RBC is affected by parasite under the microscope by qualified experts. It is a complex technique and the diagnosis depends on the experience and inside of the person who performs the examination. Malaria blood smear have been diagnosed earlier using image processing methods based on machine learning. This was not effective so far. Convolutional Neural Network (CNN) is use in this system which helps in classifying the cells present in the blood smear images as infected or uninfected

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