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Renal Cell Carcinoma Nuclear Grading Using 2d Textural Features for Kidney Images

Cancer identification system is proposed based on the features present in the kidney images. Different algorithms such as CLACHE (Contrast limited adaptive histogram equalization), GLCM (gray level Co-occurrence matrices) and SVM (support vector machine) algorithm are used for the identification of cancer. CLACHE algorithm is used for the enhancement of the image. GLCM algorithm is used to improve the overall accuracy of the system and to extract the textural features. SVM algorithm is used to classify the different grading levels to identify the cancer present in the image. Images that are acquired for the identification of cancer are noisy. Noise is removed by the ROI extraction. Then the images are enhanced using CLACHE algorithm. Once the images are enhanced, features are extracted using GLCM. 21 textural features are extracted. Out of the 21 features extracted two best features are selected. The two best features are compared with the trained features for the increase in the accuracy of the system. After that based on the features different grading levels are obtained for the identification of the cancer. Grade 1 indicates the presence of cancer in starting stage, grade 2 indicates the presence of cancer in the moderate stage, grade 3 indicates the presence of cancer in the mild stage, grade 4 indicates the presence of cancer in the severe stage. In this study, 2D textural features are extracted and using these extracted features cancer identification is done which improves the overall accuracy of the system.

Published by: Jayashree G. R, Dr. K. M Ravi Kumar, Ravi Kiran .R

Author: Jayashree G. R

Paper ID: V3I3-1377

Paper Status: published

Published: May 25, 2017

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Tumor and Edema Segmentation Using Efficient MFCM and MRG Algorithm

Momentarily, categorizing of brain tumor and segmentation is truly an exciting task in MRI. Numerous researches work in generating divergent plus interesting techniques and algorithms for this specified work of medical image processing. On behalf of enhancing a precise brain tumor extraction, we provide an effective methodology for both classification and segmentation i.e., separation of brain MRI images as well as labeling of brain MRI images in terms of edema, tumor, white matter (WM),gray matter (GM) plus cerebrospinal fluid (CSF). At this instant and in our recommended system of brain tumor detection encompasses six segments, i.e. pre-processing, filtering, Image registration, Feature extraction, Classification, Segmentation. At this moment, in case of preprocessing, the input MRI image is firstly fetched from the MRI database and as well subjected to skull stripping for rejecting the undesirable area from the image. In addition, by the utilizing Gaussian filter, the skull stripped image has been smoothened. Subsequently by utilizing Automatic image registration the filtered images are recorded into one coordinate system wherever the movement of the head is a situation often encountered during the imaging process. Shape, intensity and texture are the features that will be extricated from the registered images.On the basis of extricated features, the Brain MRI images are characterized into normal or abnormal images. Finally by utilizing the modified FCM segmentation algorithm tumor portion is extracted and edema is segmented applying modified region enhancing from the abnormal images. Therefore in case of normal image, the Gray matter, the white matter and the cerebrospinal fluid can be segmented. The outcomes are analyzed for illustrating the representation of the suggested classification plus segmentation methodology with prevailing techniques.

Published by: Rehna Kalam, M. Abdul Rahman

Author: Rehna Kalam

Paper ID: V3I3-1357

Paper Status: published

Published: May 25, 2017

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Research Paper

A Novel Technique to Remove Duplicacy of Files and Encrypt the Data Files Symmetrically In Cloud Environment

With the help of cloud computing users are allowed to store, retrieve and share their data from anywhere. Cloud computing provides sharing of hardware, software and infrastructural storage to different users at a time. Encryption of cloud is a need of new technology because clouds have data of different clients so encryption on cloud is essential for data security but cloud computation is also important so to reduce duplicacy is one more parameter , which is reduce by digest in this paper and encryption with the help of AES and blowfish.

Published by: Sandeep Kaur

Author: Sandeep Kaur

Paper ID: V3I3-1353

Paper Status: published

Published: May 25, 2017

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Replace of Asphalt with Waste Polythene in Bitumen Road

In India consumption of Plastic is 15 million tons up to 2015 and is set to be the third largest consumer of plastics in the world. India is a top 20 number country that dump maximum plastic in the ocean. As per a survey conducted by Central Pollution Control Board (CPCB) in 60 cities of the country the quantum of plastic waste generation is estimated to be 15,342.6 tons per day. Plastic is a non-biodegradable material and researchers found that the material can remain on earth for 4500 years without degradation

Published by: Mr. Yakub Ansari, Prof. F. I Chavan, Prof. M. Husain, Prof. Vajed Shaikh

Author: Mr. Yakub Ansari

Paper ID: V3I3-1352

Paper Status: published

Published: May 25, 2017

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Effect of Plastic Grocery Bags on Environment and Its Reuse

Plastic, one of the most preferred materials in today's industrial world is posing serious threat to environment and consumer's health in many direct and indirect ways.

Published by: Mr. Yakub Ansari, Prof. F. I Chavan, Prof. M. Husain

Author: Mr. Yakub Ansari

Paper ID: V3I3-1351

Paper Status: published

Published: May 25, 2017

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Review Paper

Review on Detection of Hypoglycaemia by Machine Learning Approach

The doctor concluded related to an indication that someone has the potential against diabetes mellitus (DM) the architecture of the proposed method is designed. With the data obtained from the authorities in the laboratory, the model has been adjusted. Split points and using the Gina index the best split points are identified in this paper. By identifying false split points to minimize the calculation of Gini indices a method is proposed and Gaussian fuzzy function is used because the clinical data sets are not crisp,In this paper review the different method of diabetes classification

Published by: Sannia, Shehnaz, Abhishek Bhardwaj

Author: Sannia

Paper ID: V3I3-1346

Paper Status: published

Published: May 25, 2017

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