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Zahid Ullah

Korea National University of Transportation

Ensemble CNNs for Breast Tumor Classification

Apr 11, 2023
Muhammad Umar Farooq, Zahid Ullah, Jeonghwan Gwak

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To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism. First, the regions of interest (ROIs) are obtained from the original dataset, and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are trained individually. After training, we ensemble the mechanism by summing the probabilities outputted from each network which enhances the performance up to 5%. The scheme has been validated on a public dataset and we achieved accuracy, precision, and recall 88%, 85%, and 76% respectively.

* SMA 2021: The 10th International Conference on Smart Media and Applications Gunsan Saemangeum Convention Center and Kunsan National University Gunsan-si, South Korea, September 9-11, 2021

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Higher Accurate Recognition of Handwritten Pashto Letters through Zoning Feature by using K-Nearest Neighbour and Artificial Neural Network

Apr 06, 2019
Sulaiman Khan, Hazrat Ali, Zahid Ullah, Nasru Minallah, Shahid Maqsood, Abdul Hafeez

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This paper presents a recognition system for handwritten Pashto letters. However, handwritten character recognition is a challenging task. These letters not only differ in shape and style but also vary among individuals. The recognition becomes further daunting due to the lack of standard datasets for inscribed Pashto letters. In this work, we have designed a database of moderate size, which encompasses a total of 4488 images, stemming from 102 distinguishing samples for each of the 44 letters in Pashto. The recognition framework uses zoning feature extractor followed by K-Nearest Neighbour (KNN) and Neural Network (NN) classifiers for classifying individual letter. Based on the evaluation of the proposed system, an overall classification accuracy of approximately 70.05% is achieved by using KNN while 72% is achieved by using NN.

* International Journal of Advanced Computer Science and Applications (IJACSA), 9(10), June 2018
* (IJACSA) International Journal of Advanced Computer Science and Applications,

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