F
Farhat Afza
Researcher at COMSATS Institute of Information Technology
Publications - 8
Citations - 403
Farhat Afza is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Feature selection & Computer science. The author has an hindex of 5, co-authored 6 publications receiving 161 citations.
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Journal ArticleDOI
Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images
Muhammad Sharif,Muhammad Attique Khan,Muhammad Amir Rashid,Mussarat Yasmin,Farhat Afza,Urcun John Tanik +5 more
TL;DR: A privately collected database which consists of 5500 WCE images is utilised for the evaluation of the proposed method and achieved best classification accuracy of 99.42% and precision rate of 98.51%.
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A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection
Farhat Afza,Muhammad Attique Khan,Muhammad Sharif,Seifedine Kadry,Gunasekaran Manogaran,Gunasekaran Manogaran,Tanzila Saba,Imran Ashraf,Robertas Damasevicius +8 more
TL;DR: An action recognition technique based on features fusion and best feature selection and a new Weighted Entropy-Variances approach is applied to a combined vector and selects the best of them for classification.
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Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features
Muhammad Amir Rashid,Muhammad Attique Khan,Muhammad Sharif,Mudassar Raza,Muhammad Masood Sarfraz,Farhat Afza +5 more
TL;DR: This work proposes a technique by using deep convolutional neural network (DCNN) and scale invariant features transform (SIFT) to tackle complex background, congested situtaions, and similarity among different objects classification problems.
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Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine
Farhat Afza,Muhammad Irfan Sharif,Muhammad Attique Khan,Usman Tariq,Hwan Seung Yong,Jaehyuk Cha +5 more
TL;DR: A new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed and the method’s accuracy is improved and the proposed method is computationally efficient.
Journal ArticleDOI
Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection
TL;DR: An automated system for skin lesion detection and classification based on statistical normal distribution and optimal feature selection and outperforms existing methods on selected data sets is proposed.