Arbab Masood Ahmad

Arbab Masood Ahmad
University of Engineering and Technology, Peshawar · Department of Computer System Engineering

PhD

About

13
Publications
8,913
Reads
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248
Citations
Citations since 2016
7 Research Items
202 Citations
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2016201720182019202020212022010203040
Introduction
Current Research Interests: Bio-medical Signal and Image Processing, Evolutionary Computation, Analog and digital Electronics and microcontroller systems design. Methods and Techniques: Working on mammogram segmentation and classification, Epilepsy detection; and Cell feature extraction and classification of Fine Needle Aspiration Cytology of Breast.
Additional affiliations
April 2008 - present
University of Engineering and Technology, Peshawar
Position
  • Professor (Assistant)
April 2008 - present
University of Engineering and Technology, Peshawar, Pakistan
Position
  • Professor (Assistant)
Education
April 1992 - April 1997
University of Engineering and Technology, Taxila, Pakistan
Field of study
  • Electrical Engineering

Publications

Publications (13)
Article
Full-text available
Standard method of assessing breast cancer is a triple test assessment. In this method, initially a thorough medical examination and patient history is evaluated, secondly imaging of the breast using x-rays and/or ultrasound is done and finally a preoperative cytodiagnosis is done that is either Fine Needle Aspiration Cytology (FNAC) or Core Needle...
Article
Full-text available
Artificial Intelligence (AI) and Machine Learning (ML) are envisaged to play key roles in 5G networks. Efficient radio resource management is of paramount importance for network operators. With the advent of newer technologies, infrastructure, and plans, spending significant radio resources on estimating channel conditions in mobile networks poses...
Article
Full-text available
Underwater Wireless Sensor Networks (UWSNs) face numerous challenges due to small bandwidth, longer propagation delay, limited energy resources and high deployment cost. Development of efficient routing strategies is therefore incumbent and has remained the focus of researchers. Many routing protocols have been proposed to address these challenges...
Article
Full-text available
This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained n...
Chapter
This chapter presents the work done in the field of Cartesian Genetic Programming evolved Artificial Neural Networks (CGPANN). Three types of CGPANN are presented, the Feed-forward CGPANN (FFCGPAN), Recurrent CGPANN and the CGPANN that has developmental plasticity, also called Plastic CGPANN or PCGPANN. Each of these networks is explained with the...
Conference Paper
Full-text available
We developed a system that classifies masses or microcalcifications observed in a mammogram as either benign or malignant. The system assumes prior manual segmentation of the image. The image segment is then processed for its statistical parameters and applied to a computational intelligence system for classification. We used Cartesian Genetic Prog...
Article
Full-text available
Some of the major diseases that have a high impact on the society are Cardiovascular diseases (CVDs). An important category of CVDs are the Cardiac Arrhythmias. Conventional methods of diagnosis for the disease are prone to errors and need experience on part of the diagnosing physician. For automatic detection of Cardiac Arrhythmia we develo...
Conference Paper
Full-text available
Cartesian Genetic programming Evolved Artificial Neural Network (CGPANN) is explored for classification of different types of arrhythmia and presented in this paper. Electrocardiography (ECG) signal is preprocessed to acquire important parameters and then presented to the classifier. The parameters are calculated from the location and amplitudes of...
Article
Full-text available
A fast learning neuro-evolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN) is used to detect the presence of breast cancer. Features from breast mass are extracted using fine needle aspiration (FNA) and are applied to the CGPANN for diagnosis of breast cancer. FNA data is obtained from the Wisc...
Article
A fast learning neuroevolutionary algorithm for both feedforward and recurrent networks is proposed. The method is inspired by the well known and highly effective Cartesian genetic programming (CGP) technique. The proposed method is called the CGP-based Artificial Neural Network (CGPANN). The basic idea is to replace each computational node in CGP...
Conference Paper
Full-text available
The aim of this paper is to explore the application of Neuro-Evolutionary Techniques to the diagnosis of various diseases. We applied the evolutionary technique of Cartesian Genetic programming Evolved Artificial Neural Network (CG-PANN) for the detection of three important diseases. Some cases showed excellent results while others are in the proce...

Questions

Questions (3)
Question
Hello!
I am working on segmenting cells from Fine Needle Aspiration (FNA) images of breast. Size and shape features of the cells segmented out of the microscopic sample image shall be determined. I intend to classify the sample using my algorithm based on these feature values. I am facing problem in finding a database that contains sufficient FNA images for breast cancer. If anybody have experience working in this area, please share. Furthermore what segmentation techniques can be used for this purpose.
Dr. Arbab Masood Ahmad
Question
I am working on classifying mammogram images using computational intelligence. Is there a database with images that can be opened in Windows 7. There are a few but they are supported by a Unix environment. If anyone has experience working in the field, please do share.
Question
Hello, you did an excellent job in the arrhythmia classification project. I am interested to know, how did you select the best feature set for classification by using GP? Did you apply all the features to all the tree terminals and just noted the terminal values that formed the input to the best individual, and then used the same set of features for testing?

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Projects

Projects (2)
Archived project
The goal of this project is to detect the deadliest skin cancer melanoma. Moreover, our aim is to accurately segment the skin lesions.
Project
The project is about exploring segmentation techniques for FNA breast cancer images, for feature extraction and subsequent classification.