Browsing by Author "Islam, M.T.,"

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Bio-inspired Heuristic Optimization-based Cascaded Network for Diabetic Retinopathy Screening
(Institute of Electrical and Electronics Engineers Inc., 2024-04-25) Shuvo, E.A.,; Rahman, W.,; Hossain, M.S.; Islam, M.T.,; Iqbal, M.S.
Diabetic retinopathy (DR) is a common yet fatal complication of diabetic patients in which high levels of blood sugar damage the blood vessels in the retina, the light-sensitive eye tissue crucial for human vision. Early detection and timely intervention are essential to managing DR and preventing severe vision loss. Traditionally, the examination is performed by ophthalmologists manually examining the retinal fundus images to check for signs of DR. This approach is helpful but subjective, time-consuming and tedious. Artificial intelligence (AI)-guided computer vision has recently become very compelling and practical for image analysis and diagnosis. Existing AI-based methods achieved sufficient accuracy at the cost of high computing resources and large datasets. This paper proposes a cascaded network incorporating deep learning and the traditional machine learning approaches with a bio-inspired heuristic optimization algorithm for DR detection from fundus images. The proposed method achieved sufficient accuracy (97.1%) when trained using limited data and low computing machines. The AI models for the cascaded networks were selected through an exhaustive search in which five popular CNN models were used for extracting features; the Bacterial foraging optimization (BFO) was used to determine optimal features, and seven traditional machine models were used to detect the DR. The ResNet50-BFO-SVC cascaded network was found to be most suitable in this study. The proposed cascaded network brings efficiency, accuracy, scalability, and robustness to DR screening. © 2024 IEEE.
Species classification of brassica napus based on flowers, leaves, and packets using deep neural networks
(Elsevier B.V., 2023-12) Alom, M.,; Ali, M.Y.,; Islam, M.T.,; Uddin, A.H.,; Rahman, W.
Deep learning (DL) has gradually taken the lead as the most effective approach in the agricultural fields due to the early identification and classification of plant species and diseases for improving the quality of crop production because of recent technological breakthroughs, which have had a significant impact on agriculture. Plenty of complicated problems in farming, including species classification, plant disorder identification, yield approximation, and weather and soil moisture prediction, are made simple using deep neural networks. Thus, this proposed study aims to classify Brassica Napus (B. Napus) rapeseed species based on their most significant features, like flowers, leaves, and packets. The study has adopted two types of rapeseed such as B. Rapa and B. Alba. Five contemporary deep learning-based Convolutional Neural Network (CNN) models have also been assessed for distinguishing rapeseed species. These models are DenseNet201, VGG19, InceptionV3, Xception, and ResNet50. Initially, the researchers collected data from the agricultural field, and then image pre-processing is performed to create our dataset. After that, CNN models were applied to this dataset and enumerated the experimental data accordingly. Our DenseNet201 model successfully classified both species with the highest accuracy of 100% for flowers and 97% for both packets and leaves. A comprehensive analysis with companion studies confirmed the efficacy of our preferred paradigm for the near future. Nevertheless, future studies will compare these methodologies to data from a separate metabolomics dataset from comparable crops.