Shahram Latifi

Prof. Dr. Shahram Latifi

Department of Electrical & Computer Engineering, University of Nevada, Las Vegas, NV, USA
orcid

Research Focus & Metrics

Academic Career Length

37Years (1990-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2021 in Computer Methods and Programs in Biomedicine Update
Verified
Journal Article
Open access
PRPriyanka Rajendra
Priyanka Rajendra
SLShahram Latifi
Shahram Latifi

1 October 2017
Verified
Conference Paper
Closed access
LCLina Chato
Lina Chato
SLShahram Latifi
Shahram Latifi
This paper presents a method to automatically predict the survival rate of patients with a glioma brain tumor by classifying the patients MRI image using machine learning (ML) methods. The dataset used in this study is BraTS 2017, which provides 163 samples; each sample has four sequences of MRI brain images, the overall survival time in days, and the patients age. The dataset is labeled into three classes of survivors: short-term, mid-term, and long-term. To improve the prediction results, various types of features were extracted and trained by various ML methods. Features considered included volumetric, statistical and intensity texture, histograms and deep features; ML techniques employed included support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant, tree, ensemble and logistic regression. The best prediction accuracy based on classification is achieved by using deep learning features extracted by a pre-trained convolutional neural network (CNN) and was trained by a linear discriminant.

1 January 2005 in Information Processing Letters
Verified
Journal Article
Closed access
JZJun Zheng
Jun Zheng
SLShahram Latifi
Shahram Latifi
EREmma Regentova
Emma Regentova
KLKai Luo
Kai Luo
XWXiaolong Wu
Xiaolong Wu

1 February 1996 in Journal of Parallel and Distributed Computing
Verified
Journal Article
Closed access
YRYordan Rouskov
Yordan Rouskov
SLShahram Latifi
Shahram Latifi
PSPradip K. Srimani
Pradip K. Srimani

1 May 2008 in Information Sciences
Verified
Journal Article
Closed access
XWXiaolong Wu
Xiaolong Wu
SLShahram Latifi
Shahram Latifi

1 December 2017
Verified
Conference Paper
Closed access
STShahab Tayeb
Shahab Tayeb
MPMatin Pirouz
Matin Pirouz
JSJohann Sun
Johann Sun
KHKaylee Hall
Kaylee Hall
ACAndrew Chang
Andrew Chang
SLShahram Latifi
Shahram Latifi
As the healthcare industry becomes more reliant upon electronic records, the amount of medical data available for analysis increases exponentially. While this information contains valuable statistics, the sheer volume makes it difficult to analyze without efficient algorithms. By using machine learning to classify medical data, diagnoses can become more efficient, accurate, and accessible for the public. After choosing k-Nearest Neighbors for its simplicity, we applied it to datasets compiled by the University of California, Irvine Machine Learning Repository to diagnose two conditions - chronic kidney failure and heart disease - with an accuracy of approximately 90%. In the future, similar methods can be used on a larger scale to bring ease of use to the field of medical diagnostics.