Hello, I am Sidratul Montaha

Programmer | AI Researcher

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About Me I am passionate for coding, a foodie, love to travel. Find me on Linkedin, Google Scholar, Research Gate.

Hi there, My name is Sidratul Montaha hailing from Calgary, Alberta, Canada. I am pursuing master’s in computer science at the University of Calgary. My research interest includes artificial intelligence, health informatics, medical imaging, computer vision, machine learning and deep learning. As a former research assistant at Charles Darwin University, I have contributed to significant projects for the Menzies Institute of Technology

My CV

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SkinNet-14: a deep learning framework for accurate skin cancer classification using low-resolution dermoscopy images with optimized training time

Neural Computing and Applications

Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network

Fast and efficient lung abnormality identification with explainable AI: A comprehensive framework for chest CT scan and X-ray images

Enhancing cervical cancer diagnosis with graph convolution network: AI-powered segmentation, feature analysis, and classification for early detection

Multimedia Tools and Applications

Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Journal of Imaging Informatics in Medicine

An automated decision support system to analyze malignancy patterns of breast masses employing medically relevant features of ultrasound images

Journal of Imaging Informatics in Medicine

Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features

Journal of Cancer Research and Clinical Oncology

Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation

Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model

Pixel-level image analysis to derive the broncho-artery (BA) ratio employing HRCT scans: A computer-aided approach

Intelligent Systems with Applications

Ergonomic Risk Prediction for Awkward Postures from 3D Keypoints Using Deep Learning

A novel data and model centric artificial intelligence based approach in developing high-performance named entity recognition for bengali language

Mcnn-lstm: Combining cnn and lstm to classify multi-class text in imbalanced news data

An automated broncho-arterial (ba) pair segmentation process and assessment of ba ratios in children with bronchiectasis using lung hrct scans: A pilot study

Brain tumor segmentation from 3d MRI scans using U-NeT

SN Computer Science

Using feature maps to unpack the CNN ‘Black box’theory with two medical datasets of different modality

Intelligent Systems with Applications

Automated detection of broncho-arterial pairs using CT scans employing different approaches to classify lung diseases

An effective ensemble machine learning approach to classify breast cancer based on feature selection and lesion segmentation using preprocessed mammograms

A robust framework combining image processing and deep learning hybrid model to classify cardiovascular diseases using a limited number of paper-based complex ECG images

An effective approach to address processing time and computational complexity employing modified CCT for lung disease classification

Intelligent Systems with Applications

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

Frontiers in Medicine

A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity

Timedistributed-cnn-lstm: A hybrid approach combining cnn and lstm to classify brain tumor on 3d mri scans performing ablation study

BreastNet18: a high accuracy fine-tuned VGG16 model evaluated using ablation study for diagnosing breast cancer from enhanced mammography images

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See all projects

September 7, 2024

Loss Function

August 27, 2024

Cardiac Data Annotation

February 10, 2024

Cyst Prediction

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My Resume

My area of experties are computer vision, medical imaging, TinyML, data processing & analysis.

Education Experiences

2023-Present

M.Sc. IN COMPUTER SCIENCE

University of Calgary, Calgary, Alberta, Canada. CGPA-3.80 of 4

2018-2022

B.Sc. IN COMPUTER SCIENCE AND ENGINEERING

Daffodil International University, Dhaka, Bangladesh. CGPA-3.82 of 4

August, 2023- Present

Teaching Assistant

University of Calgary, Canada.

August, 2023- Present

Research Assistant

Vision Research Lab, University of Calgary, Canada.

January, 2024- April, 2024

Mentor

AI Research School, Canada.

July, 2022- July, 2023

Research Assistant (Remote)

Charles Darwin University, Australia.

June, 2022- July, 2023

Research Coordinator

Health Informatics Research Lab, Bangladesh.

January 2021 - December 2021

Teaching Assistant

Daffodil International University, Bangladesh.

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My Skills

Python

75%

Deep Learning

75%

Machine Learning

85%

SQL

70%

Image Analysis and Preprocessing

80%

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Introduction to Structured Query Language (SQL)

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Programming for Everybody (Getting Started with Python)

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Machine Learning for All

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Mathematics for Machine Learning; Linear Algebra

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International Graduate Tuition Award

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International Graduate Recruitment Award

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Alberta Graduate Excellence Scholarship

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