Dr. Yogesh M. Rajput
@sig.ac.in
Assistant Professor, Symbiosis Institute of Geoinformatics
Symbiosis Institute of Geoinformatics, Pune
RESEARCH INTERESTS
Biomedical Image Processing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Shivani Shukla, Ajay Mittal, Yogesh Rajput, Vidya Kumbhar, and T. P. Singh
Springer Nature Singapore
Yogesh Rajput, Sonali Gaikwad, Rajesh Dhumal, and Jyotsna Gaikwad
Springer Nature Singapore
Omkar S. Bankar, Yogesh M. Rajput, Vidya Kumbhar, and T. P. Singh
IEEE
This research focuses on the analysis of the activity of the patients who are depressed (condition) and non-depressed (control) using a huge amount of time-series data which was collected using an actigraph watch (wristwatch), at every moment like zero activity, mean activity, etc. and classification of those by using machine learning models such as Decision Tree Classifier, Random Forest Classifier, and XGBoost algorithm. The primary objective was to determine the effectiveness of each algorithm in accurately classifying depression cases. The results of this research contribute to the growing body of knowledge surrounding the application of machine learning in mental health studies, particularly for depression.
Yogesh Rajput, Sonali Gaikwad, Rajesh Dhumal, and Jyotsna Gaikwad
Springer Nature Singapore
Yogesh Rajput, Shaikh Abdul Hannan, Dnyaneshwari Patil, and Ramesh Manza
Springer Singapore
Dnyaneshwari D. Patil, Ramesh R. Manza, Rakesh J. Ramteke, Yogesh Rajput, and Sanjay Harke
Springer Singapore
Yogesh Rajput, Shaikh Abdul Hannan, Mohammad Eid Alzahrani, Dnyaneshwari Patil, and Ramesh Manza
Springer Singapore
Anupriya K. Kamble, Ramesh R. Manza, Yogesh M. Rajput, and Shaikh Abdul Hannan
IEEE
Diabetes Mellitus (DM), commonly referred as diabetes is a disorder that most of the people suffer from and which also leads to death many of the times. Type 1, Type 2 and Gestational diabetes are the types of diabetes. The present study is of patients suffering from Type 1 DM. In Type 1 diabetes a patient has to take external insulin to maintain the blood glucose level (BGL). Insulin is a hormone produced in the pancreas which maintains the BGL of a person. BGL is responsible for a person's daily activity. Within a specified target range the blood glucose level has to be maintained. Thus the blood glucose monitoring helps one to link between blood glucose, food, exercise and insulin. The readings of the blood glucose level determine the best management strategy for diabetes. The complications occurring in diabetes reduces by maintaining good blood glucose control. Four to five types of insulin are available in which our focus is on Regular insulin and NPH insulin. Regular insulin is a short acting insulin and NPH insulin is an intermediate acting insulin. The association detection of first fifteen patients by doing statistical study has already been done in which no association was found. This study has been done on the next fifteen patients dataset, to verify same result are obtained or result differs from it.
Yogesh M. Rajput, Ramesh R. Manza, Rathod D. Deepali, Manjiri B. Patwari, Manoj Saswade, and Neha Deshpande
Springer India
Gangadevi C. Bedke, Ramesh R. Manza, Dnyaneshwari D. Patil, and Yogesh M. Rajput
IEEE
Glaucoma is an eye disease. In glaucoma retinal nerve fiber layers are damaged and if it is not treated earlier then it can cause permanent vision loss. This paper represents algorithm for detection of glaucoma using retinal nerve fiber layers. For this work we have used 2D median filter and HAAR wavelet transform methods. For this work we have also used Drishti-GS dataset which contains 101 glaucomatous images and HRF (High Resolution Fundus image) database. We have extracted the retinal nerve fiber layer Arteries. Then we have calculated its area and diameter. On the normal database we got the 100% result. We got 71.28% accuracy on glaucomatous images and when we have combined the normal and glaucomatous images then we got the 62.06% accuracy.
Yogesh M. Rajput, Ramesh R. Manza, Manjiri B. Patwari, Deepali D. Rathod, Prashant L. Borde, and Pravin L. Yannawar
IEEE
WHO predicts that in year 2012 there are about 347 million people worldwide have diabetes, more than 80% of diabetes deaths occur in different countries. WHO projects that diabetes will be the 7th major cause leading death in 2030. Diabetic Retinopathy caused by leakage of blood or fluid from the retinal blood vessels and it will damage the retina. Non-proliferative diabetic retinopathy (NPDR) is an early stage of diabetic retinopathy and it is categorized into three stages they are mild, moderate and sever NPDR. The characteristic of the Mild; is specified by the presence of minimum microaneurysm, Moderate; specifies the presence of hemorrhages, microaneurysms, and hard exudates where as Severe; determine on the blockage of vessels, depriving several areas of the retina. With their blood supply. These areas of the retina send signals to the body to grow new blood vessels for nourishment. The proposed algorithm tested on online databases like STARE, DRIVE, DiarectDB0, DiarectDB1 and SASWADE (the database collected during the research work). The statistical techniques were applied on NPDR lesion and calculate the mean, variance, standard deviation, & correlation for classification. K-means clustering have been applied on the dataset with extracted features 95% of correct classification have been achieved.
Manjiri B. Patwari, Ramesh R. Manza, Yogesh M. Rajput, Manoj Saswade, and Neha Deshpande
IEEE
Biometric identifiers are the unique, measurable characteristics used to tag and describe individuals. Physiological characteristics are related to the shape of the body. Examples of biometric identifications are, fingerprint, face, DNA, Palm print, hand geometry, iris recognition, and retina. Human retina is another source of biometric system which provides the most reliable and stable means of authentication. We propose a new algorithm for the detection and measurement of blood vessels of the retina and finding the bifurcation points of blood vessels for personal identification. A minutiae technique for finding bifurcation points of the extracted blood vessels and according to bifurcation points identifies the individual person. Performance of these techniques is tested using the database from Dr. Manoj Saswade and Dr. Neha Deshpande (300 Images). This algorithm achieves a true positive rate of 98%, false positive rate of 20%, and accuracy score of 0.9702 and also classification down through Statistical Techniques.
Patwari Manjiri, Manza Ramesh, Rajput Yogesh, Saswade Manoj, and Deshpande Neha
Springer International Publishing