Zeeshan Abbas
Air University (Islamabad)
22 Papers
42 Citations
Zeeshan Abbas is an academic researcher from Air University (Islamabad). The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 6, co-authored 11 publications. Previous affiliations of Zeeshan Abbas include Chonbuk National University & COMSATS Institute of Information Technology.
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Papers
Classification of Diabetic Retinopathy Images Based on Customised CNN Architecture
Mobeen-ur-Rehman,Sharzil Haris Khan,Zeeshan Abbas,S.M. Danish Rizvi +3 more
- 01 Feb 2019
TL;DR: This paper implements automated tools to detect Diabetic Retinopathy using CNN approach for the classification of DR images, which has shown promising result of sensitivity, specificity and accuracy.
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Classification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network
Mobeen Ur Rehman,Sharzil Haris Khan,S.M. Danish Rizvi,Zeeshan Abbas,Adil Zafar +4 more
- 05 Jul 2018
TL;DR: The groundwork for detection of skin lesions with cancerous inclination by segmentation and subsequent application of Convolution Neural Network on dermoscopy images is presented.
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SpineNet-6mA: A Novel Deep Learning Tool for Predicting DNA N6-Methyladenine Sites in Genomes
TL;DR: A novel deep learning based model based on a special architecture called SpinalNet is proposed for the prediction of DNA N6-methyladenine sites in rice genomes and produces better scores than existing models regarding all evaluation parameters.
An Efficient Gray-Level Co-Occurrence Matrix (GLCM) based Approach Towards Classification of Skin Lesion
Zeeshan Abbas,Mobeen Ur Rehman,Shahzaib Najam,S.M. Danish Rizvi +3 more
- 01 Feb 2019
TL;DR: The technique proposed in this paper is high effective as being compared with latest published techniques proposed in previous researches and has achieved the accuracy of 99.02%.
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TS-m6A-DL: Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model.
TL;DR: Wang et al. as mentioned in this paper proposed a universal model using deep neural network (DNN) and named it TS-m6A-DL, which can classify m6A sites in several tissues of humans (Homo sapiens), mice (Mus musculus), and rats (Rattus norvegicus).
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