Usman Saif

Work place: School of System and Technology, University of Management and Technology Lahore, Pakistan



Research Interests: Natural Language Processing, Robotics, Computer Architecture and Organization, Data Structures and Algorithms


Usman Saif is currently a lead Software Engineer at Codility Solutions. He remained a part of multiple projects, completed in this organization. He completed his master’s in software engineering from University of Management and Technology, Lahore, Pakistan in 2019 and worked in multiple data science projects throughout his masters and career. He completed his Bachelors in Software Engineering from UMT, Lahore in 2016.

He did his thesis in Deep Learning, Machine Learning and Artificial Intelligence domain. Some of his other areas of interest are Natural Language Processing and Robotics.

Author Articles
Classification of Images of Skin Lesion Using Deep Learning

By Momina Shaheen Usman Saif Shahid M. Awan Faizan Ahmad Aimen Anum

DOI:, Pub. Date: 8 Apr. 2023

Skin cancer is among common and rapidly increasing human malignancies, which can be diagnosed visually. The diagnosis begins with preliminary medical screening and by dermoscopic examination, histopathological examination, and proceeding to the biopsy. This screening and diagnosis can be automated using machine learning tools and techniques. Artificial neural networks are helping a lot in medical diagnosis applications. In this research, skin images are classified into 7 different classes of skin cancer using deep learning methodology, then analyzed the results w.r.t to their respective precision, recall, support, and accuracy to find its practical applicability. This model is efficient in comparison to the detection of skin cancer with human eyes. Human eyes detection can be 79% accurate at most. Thus, having a scientific method of diagnosis can help the doctors and practitioners to accurately identify the cancer and its type. The model provides 80% accuracy on average for all 7 types of skin diseases, thus being more reliable than human eye examination. It will help the doctors to diagnose the skin diseases more confidently. The model has only 2 misclassified predictions for Basal cell carcinoma and Vascular lesions. However, Actinic keratosis diagnosis is most accurately predicted.

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