DEEP CONVOLUTIONAL NEURAL STRATEGY FOR DETECTION AND PREDICTION OF MELANOMA SKIN CANCER

2021 ◽  
Vol 33 (01) ◽  
pp. 2050045
Author(s):  
Ravi Dandu ◽  
Jayakameshwaraiah ◽  
Y. B. Ravi Kumar
Keyword(s):  
Learning Strategy ◽  
Research Work ◽  
Texture Features ◽  
Representation Learning ◽  
Features Extraction ◽  
Blue Channel ◽  
Segmentation Accuracy ◽  
Channel Information ◽  
Classification Of Images

The research work has focused on detection and prediction of melanoma which is done by subjecting to features extraction, where the features of an image consisting of melanoma regions are detected by analysis and this analysis is done by considering the features like color and texture-based features learning strategy. These features are extracted by combining color and texture-based features extraction with deep convolutional features representation learning strategy. The colors of images are extracted by representing the colors of different channels into red, green and blue channel information. The combination of texture features extraction with color-based features extraction in addition to Alex net features extraction learning has made the system more robust and efficient toward the segmentation and classification of images. Further, the erected method involves convoluting the features of extracted information with color and texture-based method which has led our system to full convolution neural networks with images features extraction. The melanoma is detected and segmented with watershed segmentation, these segmented features are subjected to the proposed features extraction method, where the features are extracted by combining the methods of texture with color-based information. These colors are made available to the proposed method by analyzing the regions of melanoma images. The erected method does the task of features extraction by Weber law descriptors in combination with red, green, blue channels information extracted from features representation learning. The proposed method has yielded an accuracy of 94.12% of segmentation accuracy and a classification accuracy of 94.32% with respect to various other classification techniques.

scholarly journals Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Rajesh Kumar ◽  
Rajeev Srivastava ◽  
Subodh Srivastava
Keyword(s):  
Texture Features ◽  
Histogram Equalization ◽  
Gray Level ◽  
Segmentation Methods ◽  
Segmented Images ◽  
Clinically Significant ◽  
Classification Of Images ◽  
Nearest Neighborhood ◽  
Neighborhood Method

A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law’s Texture Energy based features, Tamura’s features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images.

Recognition and Classification of Food Grains, Fruits and Flowers Using Machine Vision

2009 ◽  
Vol 5 (4) ◽  
Author(s):  
Dayanand G Savakar ◽  
Basavaraj S Anami
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Texture Features ◽  
Morphological Features ◽  
Machine Vision System ◽  
Food Grains ◽  
Colour Features ◽  
Classification Of Images ◽  
Color Texture

In this paper, we have presented different methodologies devised for recognition and classification of images of agricultural/horticultural produce. A classifier based on BPNN is developed which uses the color, texture and morphological features to recognize and classify the different agricultural/horticultural produce. Even though these features have given different accuracies in isolation for varieties of food grains, mangoes and jasmine flowers, the combination of features proved to be very effective. The average recognition and classification accuracies using colour features are 87.5%, 78.4% and 75.7% for food grains, mango and jasmine flowers, respectively, and the average accuracies have increased to 90.8%, 80.2% and 85.8% for food grains, mangoes and jasmine flowers ,respectively, using texture features. The average accuracies have increased to 94.1%, 84.0% and 90.1% for food grains, mangoes and jasmine flowers, respectively. The results are encouraging and promise a good machine vision system in the area of recognition and classification of agricultural/horticultural produce.

scholarly journals A Novel Multiinstance Learning Approach for Liver Cancer Recognition on Abdominal CT Images Based on CPSO-SVM and IO

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Huiyan Jiang ◽  
Ruiping Zheng ◽  
Dehui Yi ◽  
Di Zhao
Keyword(s):  
Liver Cancer ◽  
Texture Features ◽  
Ct Images ◽  
Support Vector ◽  
Abdominal Ct ◽  
Segmentation Accuracy ◽  
Multiple Regions ◽  
Tumor Region ◽  
Liver Region

A novel multi-instance learning (MIL) method is proposed to recognize liver cancer with abdominal CT images based on instance optimization (IO) and support vector machine with parameters optimized by a combination algorithm of particle swarm optimization and local optimization (CPSO-SVM). Introducing MIL into liver cancer recognition can solve the problem of multiple regions of interest classification. The images we use in the experiments are liver CT images extracted from abdominal CT images. The proposed method consists of two main steps: (1) obtaining the key instances through IO by texture features and a classification threshold in classification of instances with CPSO-SVM and (2) predicting unknown samples with the key instances and the classification threshold. By extracting the instances equally based on the entire image, the proposed method can ignore the procedure of tumor region segmentation and lower the demand of segmentation accuracy of liver region. The normal SVM method and two MIL algorithms, Citation-kNN algorithm and WEMISVM algorithm, have been chosen as comparing algorithms. The experimental results show that the proposed method can effectively recognize liver cancer images from two kinds of cancer CT images and greatly improve the recognition accuracy.

scholarly journals Classification of mammograms based on features extraction techniques using support vector machine

2020 ◽  
Vol 2 (3) ◽  
pp. 121-131
Author(s):  
Enas Mohammed Hussein Saeed ◽  
Hayder Adnan Saleh ◽  
Enam Azez Khalel
Keyword(s):  
Region Growing ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Features Extraction ◽  
Support Vector ◽  
Global Threshold ◽  
Medical City ◽  
Analysis Society ◽  
Occurrence Matrix

Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a Hybrid Bounding Box and Region Growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM), Finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.

scholarly journals GENERALIZATION OF THE COOCCURRENCE MATRIX FOR COLOUR IMAGES: APPLICATION TO COLOUR TEXTURE CLASSIFICATION

2011 ◽  
Vol 23 (1) ◽  
pp. 63 ◽  
Author(s):  
Vincent Arvis ◽  
Christophe Debain ◽  
Michel Berducat ◽  
Albert Benassi
Keyword(s):  
Texture Classification ◽  
Computation Time ◽  
Texture Features ◽  
Grey Scale ◽  
Colour Image ◽  
Good Classification ◽  
Real Time Applications ◽  
Classification Of Images ◽  
Multispectral Method

Three different approaches to colour texture analysis are tested on the classification of images from the VisTex and Outex databases. All the methods tested are based on extensions of the cooccurrence matrix method. The first method is a multispectral extension since cooccurrence matrices are computed both between and within the colour bands. The second uses joint colour-texture features: colour features are added to grey scale texture features in the entry of the classifier. The last uses grey scale texture features computed on a previously quantized colour image. Results show that the multispectral method gives the best percentages of good classification (VisTex: 97.9%, Outex: 94.9%). The joint colour-texture method is not far from it (VisTex: 96.8%, Outex: 91.0%), but the quantization method is not very good (VisTex:83.6%, Outex:68.4%). Each method is decomposed to try to understand each one deeper, and computation time is estimated to show that multispectral method is fast enough to be used in most real time applications.

PROBLEMS OF CLASSIFICATION OF CULTURAL HERITAGE OBJECTS

2020 ◽  
Vol 10 (2) ◽  
pp. 158-168
Author(s):  
SVETLANA IVANOVA ◽  
Keyword(s):  
Cultural Heritage ◽  
Cultural Values ◽  
Research Work ◽  
Cultural Property ◽  
Cultural Inheritance ◽  
Federal Laws ◽  
Cultural Heritage Objects ◽  
Cultural Monuments

The purpose of the research work is to analyze the norms of Federal laws, as well as the laws of the Russian Federation's constituent entities, devoted to the definitions and classification of the concepts “cultural heritage”, “historical and cultural monuments”, “cultural values”. Conclusions obtained in the course of the research: based on the study of current legislation, it is concluded that the definitions of “cultural values”, “cultural property”, “objects of cultural inheritance” contained in various normative legal acts differ in content. Based on the research, the author proposes the concept of “cultural values”.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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