Md. Masudul Islam
@bubt.edu.bd
Assistant Professor
Bangladesh University of Business and Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Computer Science, Multidisciplinary, Information Systems
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Md. Masudul Islam and Md. Ripon Sheikh
Elsevier BV
Md. Darun Nayeem, Saima Zannat Sraboni, Shejuti Shithi Biswas, and Md. Masudul Islam
Elsevier BV
Md. Darun Nayeem, Nusrat Jahan Nisita, Md. Masudul Islam, Md. Saifur Rahman, and A. B. M. Shawkat Ali
Springer Science and Business Media LLC
Rup Kumar Bharati, Md. Masudul Islam, Md Ripon Sheikh, and Galib Muhammad Shahriar Himel
Elsevier BV
Galib Muhammad Shahriar Himel and Md. Masudul Islam
Springer Science and Business Media LLC
Abstract Image classification using deep learning has gained significant attention, with various datasets available for benchmarking algorithms and pre-trained models. This study focuses on the Microsoft ASIRRA dataset, renowned for its quality and benchmark standards, to compare different pre-trained models. Through experimentation with optimizers, loss functions, and hyperparameters, this research aimed to enhance model performance. Notably, this study achieved significant accuracy improvements with minimal modifications to the training process. Experiments were conducted across three computer architectures, yielding superior accuracy results compared to previous studies on this dataset. The NASNet Large model emerged with the highest accuracy at 99.65%. The findings of this research demonstrate the effectiveness of hyperparameter tuning for renowned pre-trained models, suggesting optimal settings for improved classification accuracy. This study underscores the potential of deep learning approaches in achieving superior performance by hyperparameter tuning for image classification tasks.
Md. Masudul Islam, Galib Muhammad Shahriar Himel, Golam Moazzam, and Mohammad Shorif Uddin
Elsevier BV
Md. Masudul Islam, Galib Muhammad Shahriar Himel, Md. Golam Moazzam, and Mohammad Shorif Uddin
Elsevier BV
Galib Muhammad Shahriar Himel, Md. Masudul Islam, Md. Nasimul Kader, and Mustafizur Rahman
Springer Science and Business Media LLC
Abstract This article introduces an Artificial Intelligent-driven system for Galliformes Farm Management, consulting, and disease control. Comprising both a web-based mobile app and a website, the system integrates physical electronic devices to regulate intelligent automation. The application employs Artificial Neural Network and computer vision for the identification of breeds and the recognition of diseases in galliform birds, utilizing TensorFlow for real-time detection. It notifies local farmers about bird flu infections with an integrated infection map using Google's map Application Programming Interface. The system also controls farm temperature and humidity through microcontrollers and sensors, offering consulting features such as cost calculation, food chart generation, and drug suggestions.
Galib Muhammad Shahriar Himel, Md. Masudul Islam, and Mijanur Rahaman
Elsevier BV
, Galib Muhammad Shahriar Himel, and Md. Masudul Islam
MECS Publisher
Galib Muhammad Shahriar Himel, Md. Shourov Hasan, Umme Sadia Salsabil, and Md. Masudul Islam
Elsevier BV
Md. Masudul Islam, Galib Muhammad Shahriar Himel, Mohammad Shorif Uddin, and Md. Golam Moazzam
Elsevier BV
Md Ripon Sheikh, Md. Anwar Hossain, Moazzem Hossain, Md. Masudul Islam, and Galib Muhammad Shahriar Himel
Elsevier BV
Md Ripon Sheikh, Md. Masudul Islam, and Galib Muhammad Shahriar Himel
Elsevier BV
Galib Muhammad Shahriar Himel, Md. Masudul Islam, and Mijanur Rahaman
Elsevier BV
Galib Muhammad Shahriar Himel and Md Masudul Islam
Elsevier BV
Galib Muhammad Shahriar Himel, Md. Masudul Islam, Kh. Abdullah Al-Aff, Shams Ibne Karim, and Md. Kabir Uddin Sikder
Wiley
Skin cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. In recent years, deep learning models have shown remarkable success in various computer vision tasks, including image classification. In this research study, we introduce an approach for skin cancer classification using vision transformer, a state-of-the-art deep learning architecture that has demonstrated exceptional performance in diverse image analysis tasks. The study utilizes the HAM10000 dataset; a publicly available dataset comprising 10,015 skin lesion images classified into two categories: benign (6705 images) and malignant (3310 images). This dataset consists of high-resolution images captured using dermatoscopes and carefully annotated by expert dermatologists. Preprocessing techniques, such as normalization and augmentation, are applied to enhance the robustness and generalization of the model. The vision transformer architecture is adapted to the skin cancer classification task. The model leverages the self-attention mechanism to capture intricate spatial dependencies and long-range dependencies within the images, enabling it to effectively learn relevant features for accurate classification. Segment Anything Model (SAM) is employed to segment the cancerous areas from the images; achieving an IOU of 96.01% and Dice coefficient of 98.14% and then various pretrained models are used for classification using vision transformer architecture. Extensive experiments and evaluations are conducted to assess the performance of our approach. The results demonstrate the superiority of the vision transformer model over traditional deep learning architectures in skin cancer classification in general with some exceptions. Upon experimenting on six different models, ViT-Google, ViT-MAE, ViT-ResNet50, ViT-VAN, ViT-BEiT, and ViT-DiT, we found out that the ML approach achieves 96.15% accuracy using Google’s ViT patch-32 model with a low false negative ratio on the test dataset, showcasing its potential as an effective tool for aiding dermatologists in the diagnosis of skin cancer.
Mijanur Rahaman, Md.Masudul Islam, and Md.Saifur Rahman
Springer Nature Singapore
Md.Atiqur Rahman, M. M. Fazle Rabbi, Md. Mijanur Rahman, Md. Masudul Islam, and Md. Rashedul Islam
IEEE