World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
38
Citations
4053
World Ranking
736
National Ranking
8

Computer Science

D-Index
40
Citations
5009
World Ranking
9438
National Ranking
35

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Tallha Akram is affiliated with COMSATS University Islamabad in Pakistan. Their research focuses primarily on the intersection of medicine and computer science, with significant work in subfields including computer vision and pattern recognition, oncology, artificial intelligence, plant science, and epidemiology.

Their main areas of study and research topics include:

  • Cutaneous Melanoma Detection and Management
  • AI in cancer detection
  • Smart Agriculture and AI
  • Nonmelanoma Skin Cancer Studies
  • Spectroscopy and Chemometric Analyses
  • COVID-19 diagnosis using AI
  • Advanced Image and Video Retrieval Techniques

Tallha Akram has contributed to a number of frequently published venues, reflecting a strong presence in both engineering and medical domains. These venues include:

  • Computers, materials & continua/Computers, materials & continua (Print)
  • IEEE Access
  • Diagnostics
  • International Journal of Imaging Systems and Technology
  • Sensors

Their recent publications focus on skin lesion detection and classification, utilizing deep learning and image processing techniques. Selected papers include:

  • Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization, 2021, Diagnostics
  • Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework, 2021, Pattern Recognition Letters
  • Pixels to Classes: Intelligent Learning Framework for Multiclass Skin Lesion Localization and Classification, 2021, Computers & Electrical Engineering
  • Multi-Class Skin Lesion Detection and Classification via Teledermatology, 2021, IEEE Journal of Biomedical and Health Informatics
  • A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals, 2022, Sensors

Frequent coauthors of Tallha Akram illustrate collaboration with researchers in related fields, including:

  • Muhammad Attique Khan
  • Syed Rameez Naqvi
  • Majed Alhaisoni
  • Anas Alsuhaibani
  • Muhammad Sharif

Best Publications

  • Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization

    Muhammad Attique Khan;Muhammad Sharif;Tallha Akram;Robertas Damaševičius

  • CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features

    Muhammad Attique Khan;Tallha Akram;Muhammad Sharif;Muhammad Awais

  • Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework

    Muhammad Attique Khan;Tallha Akram;Yu-Dong Zhang;Muhammad Sharif

  • Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection.

    Muhammad A. Khan;Ikram U. Lali;Amjad Rehman;Mubashar Ishaq

  • Multi-Class Skin Lesion Detection and Classification via Teledermatology

    Muhammad Attique Khan;Khan Muhammad;Muhammad Sharif;Tallha Akram

  • Hand-crafted and deep convolutional neural network features fusion and selection strategy: An application to intelligent human action recognition

    Muhammad Attique Khan;Muhammad Attique Khan;Muhammad Sharif;Tallha Akram;Mudassar Raza

  • Pixels to Classes: Intelligent Learning Framework for Multiclass Skin Lesion Localization and Classification

    Muhammad Attique Khan;Yu-Dong Zhang;Yu-Dong Zhang;Muhammad Sharif;Tallha Akram

  • Developed Newton-Raphson based deep features selection framework for skin lesion recognition

    Muhammad Attique Khan;Muhammad Sharif;Tallha Akram;Syed Ahmad Chan Bukhari

  • A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection

    Muhammad Sharif;Muhammad Attique Khan;Tallha Akram;Muhammad Younus Javed

  • License number plate recognition system using entropy-based features selection approach with SVM

    Muhammad Attique Khan;Muhammad Sharif;Muhammad Younus Javed;Tallha Akram

  • An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

    M. Attique Khan;Tallha Akram;Muhammad Sharif;Aamir Shahzad

  • Intelligent fusion-assisted skin lesion localization and classification for smart healthcare

    Muhammad Attique Khan;Khan Muhammad;Muhammad Sharif;Tallha Akram

  • Lungs nodule detection framework from computed tomography images using support vector machine.

    Sajid A. Khan;Sajid A. Khan;Muhammad Nazir;Muhammad A. Khan;Tanzila Saba

  • A two-stream deep neural network-based intelligent system for complex skin cancer types classification

    Muhammad Attique Khan;Muhammad Sharif;Tallha Akram;Seifedine Kadry

  • An implementation of optimized framework for action classification using multilayers neural network on selected fused features

    Muhammad Attique Khan;Muhammad Attique Khan;Tallha Akram;Muhammad Sharif;Muhammad Younus Javed

  • Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

    Muhammad Attique Khan;Tallha Akram;Muhammad Sharif;Tanzila Saba

  • LSTM Neural Network Based Forecasting Model for Wheat Production in Pakistan

    Sajjad Ali Haider;Syed Rameez Naqvi;Tallha Akram;Gulfam Ahmad Umar

  • An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection

    Muhammad Attique Khan;Muhammad Attique Khan;Tallha Akram;Muhammad Sharif;Kashif Javed

  • Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine.

    Muhammad Attique Khan;Seifedine Nimer Kadry;Yudong Zhang;Tallha Akram

  • Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features.

    Sumair Aziz;Muhammad Umar Khan;Majed Alhaisoni;Tallha Akram

  • Skin lesion segmentation and recognition using multichannel saliency estimation and M-SVM on selected serially fused features

    Tallha Akram;Muhammad Attique Khan;Muhammad Sharif;Mussarat Yasmin

Frequent Co-Authors

Muhammad Attique Khan
Muhammad Attique Khan Prince Mohammad bin Fahd University
Muhammad Sharif
Muhammad Sharif COMSATS University Islamabad
Tanzila Saba
Tanzila Saba Prince Sultan University
Muhammad Sharif
Muhammad Sharif King Fahd University of Petroleum and Minerals
Amjad Rehman
Amjad Rehman Prince Sultan University
Seifedine Kadry
Seifedine Kadry Lebanese American University
Mussarat Yasmin
Mussarat Yasmin University of Gujrat
Yudong Zhang
Yudong Zhang University of Leicester
Yunyoung Nam
Yunyoung Nam Soonchunhyang University
Ching-Hsien Hsu
Ching-Hsien Hsu Asia University Taiwan

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