M
Muhammad Farhan Khan
Publications - 12
Citations - 165
Muhammad Farhan Khan is an academic researcher. The author has contributed to research in topics: Computer science & Cancer. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.
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Journal ArticleDOI
Prediction of diabetes empowered with fused machine learning
Usama Ali Ahmed,Ghassan F. Issa,Muhammad Adnan Khan,Shabib Aftab,Muhammad Farhan Khan,Raed A. Said,Taher M. Ghazal,Munir Uddin Ahmad +7 more
TL;DR: A model using a fused machine learning approach for diabetes prediction based on the patient’s real-time medical record has a prediction accuracy of 94.87, which is higher than the previously published methods.
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Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning
Muhammad Adnan Khan,Sagheer Abbas,Ayesha Atta,Allah Ditta,Hani Alquhayz,Muhammad Farhan Khan,Atta-ur-Rahman,Rizwan Ali Naqvi +7 more
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Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning
Atta-ur-Rahman,Abdullah Alqahtani,Nahier Aldhafferi,Muhammad Umar Nasir,Muhammad Farhan Khan,Muhammad Attique Khan,Amir Mosavi +6 more
TL;DR: The proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model achieved higher classification accuracy.
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Breast Cancer Detection and Classification Empowered With Transfer Learning
Sahar Arooj,Atta-ur-Rahman,Muhammad Zubair,Muhammad Farhan Khan,Khalid Alissa,Muhammad Attique Khan,Amir Mosavi +6 more
TL;DR: The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.
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Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning
Taher M. Ghazal,S.J. Hussain,Muhammad Farhan Khan,Muhammad Attique Khan,Raed A. Said,Munir Uddin Ahmad +5 more
TL;DR: An automated and reliable system for the classification of malignant and benign tumors is developed and the accuracy achieved is 87.1%, which is higher than traditional methods of classification.