A. B. M. Aowlad Hossain

Work place: Department of Electronics and Communication Engg., Khulna University of Engineering & Technology, Bangladesh

E-mail: aowlad0403@ece.kuet.ac.bd

Website: https://orcid.org/0000-0002-2559-2781

Research Interests: Medical Image Computing, Image Processing, Image Manipulation, Image Compression, Computer Architecture and Organization, Computer systems and computational processes, Signal Processing, Data Structures and Algorithms


Prof. Dr. A. B. M. Aowlad Hossain is a Professor of the Department of Electronics and Communication Engineering in Khulna University of Engineering & Technology, Bangladesh. He completed his PhD in Biomedical Engineering form Kyung Hee University, Republic of Korea in 2012. He obtained his M.Sc. in Electrical and Electronic Engineering Degree from Bangladesh University of Engineering & Technology in 2005 and B.Sc. in Electrical and Electronic Engineering Degree from Khulna University of Engineering & Technology (KUET) in 2002. He is an active researcher in the fields of Biomedical Signal and Image Processing, Elastrographic Imaging, Medical Imaging Systems, and Computer Aided Diagnosis etc. Prof. Dr. Hossain has published about 50 research papers in different journals and conference proceedings. He was awarded Best Paper Awards in two international conferences. He is technical committee member of different conferences and journals. Prof. Dr. Hossain is a life fellow of Institution of Engineers, Bangladesh (IEB) and member of Institute of Electrical and Electronics Engineers (IEEE).

Author Articles
Breast Cancer Classification from Ultrasound Images using VGG16 Model based Transfer Learning

By A. B. M. Aowlad Hossain Jannatul Kamrun Nisha Fatematuj Johora

DOI: https://doi.org/10.5815/ijigsp.2023.01.02, Pub. Date: 8 Feb. 2023

Ultrasound based breast screening is gaining attention recently especially for dense breast. The technological advancement, cancer awareness, and cost-safety-availability benefits lead rapid rise of breast ultrasound market. The irregular shape, intensity variation, and additional blood vessels of malignant cancer are distinguishable in ultrasound images from the benign phase. However, classification of breast cancer using ultrasound images is a difficult process owing to speckle noise and complex textures of breast. In this paper, a breast cancer classification method is presented using VGG16 model based transfer learning approach. We have used median filter to despeckle the images. The layers for convolution process of the pretrained VGG16 model along with the maxpooling layers have been used as feature extractor and a proposed fully connected two layers deep neural network has been designed as classifier. Adam optimizer is used with learning rate of 0.001 and binary cross-entropy is chosen as the loss function for model optimization. Dropout of hidden layers is used to avoid overfitting. Breast Ultrasound images from two databases (total 897 images) have been combined to train, validate and test the performance and generalization strength of the classifier. Experimental results showed the training accuracy as 98.2% and testing accuracy as 91% for blind testing data with a reduced of computational complexity. Gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions localization effort at the final convolutional layer and found as noteworthy. The outcomes of this work might be useful for the clinical applications of breast cancer diagnosis.

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Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering

By Rupak Bhakta A. B. M. Aowlad Hossain

DOI: https://doi.org/10.5815/ijigsp.2020.01.05, Pub. Date: 8 Feb. 2020

Lung tumor is the result of abnormal and uncontrolled cell division and growth in lung region. Earlier detection and staging of lung tumor is of great importance to increase the survival rate of the suffered patients. In this paper, a fast and robust Fuzzy c-means clustering method is used for segmenting the tumor region from lung CT images. Morphological reconstruction process is performed prior to Fuzzy c-means clustering to achieve robustness against noises. The computational efficiency is improved through median filtering of membership partition. Tumor masks are then reconstructed using surface based and shape based filtering. Different features are extracted from the segmented tumor region including maximum diameter and the tumor stage is determined according to the tumor staging system of American Joint Commission on Cancer. 3D shape of the segmented tumor is reconstructed from series of 2D CT slices for volume measurement. The accuracy of the proposed system is found as 92.72% for 55 randomly selected images from the RIDER Lung CT dataset of Cancer imaging archive. Lower complexity in terms of iterations and connected components as well as better noise robustness are found in comparison with conventional Fuzzy c-means and k-means clustering techniques.

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