Browsing by Author "Islam, Md Saidul"

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Classification of Breast Cancer Cell Images using Multiple Convolution Neural Network Architectures
(Scopus, 2021) Tasnim, Zarrin; Shamrat, F. M. Javed Mehedi; Islam, Md Saidul; Rahman, Md.Tareq; Aronya, Biraj Saha; Muna, Jannatun Naeem; Billah, Md. Masum
Abstract: Breast cancer is a malignant tumor that affects women. It is the most prevalent cancer in women, affecting about 10% of all women at any point in their lives. The development of breast cancer begins in the lobules or ducts of the cells. Early detection and prevention are the best ways to stop this cancer from spreading. In this study, five Convolution Neural Network (CNN) models are used to process image data of breast cells. Alex Net, InceptionV3, GoogLeNet, VGG19 and Exception models are used for the classification of Invasive Ductal Carcinoma, IDC and Non-Invasive Ductal Carcinoma (Non-IDC) cells. The models are trained and tested at different epochs to record the learning rate. It is observed from the study that with higher epochs, the data loss decreases and accuracy increases. The accuracy of InceptionV3 and Exception is 92.48% and 90.72% respectively. Likewise, VGG19 and Alex Net have fairly close accuracy of 94.83% and 96.74%. However, GoogLeNet dominates over the other implemented models with the highest accuracy of 97.80%. The GoogLeNet model performs with high accuracy and precision in detecting IDC cells responsible for breast cancer.
Face Mask Detection Using Convolutional Neural Network (CNN) to Reduce the Spread of Covid-19
(2021 5th International Conference Trends in Electronics and Informatics (ICOEI), IEEE, 2021-06-21) Shamrat, F.M. Javed Mehedi; Chakraborty, Sovon; Billah, Md. Masum; Jubair, Md. Al; Islam, Md Saidul; Ranjan, Rumesh
The COVID-19 coronavirus pandemic is wreaking havoc on the world's health. The healthcare sector is in a state of disaster. Many precautionary steps have been taken to prevent the spread of this disease, including the usage of a mask, which is strongly recommended by the World Health Organization (WHO). In this paper, we used three deep learning methods for face mask detection, including Max pooling, Average pooling, and MobileNetV2 architecture, and showed the methods detection accuracy. A dataset containing 1845 images from various sources and 120 co-author pictures taken with a webcam and a mobile phone camera is used to train a deep learning architecture. The Max pooling achieved 96.49% training accuracy and validation accuracy is 98.67%. Besides, the Average pooling achieved 95.190/0 training accuracy and validation accuracy is 96.23%. MobileNetV2 architecture gained the highest accuracy 99.72% for training and 99.82 % for validation.
Implementing E-Commerce Mobile and Web Application for Agricultural Products
(Daffodil International University, 2022-06-04) Chakraborty, Sovon; Shamrat, F. M. Javed Mehedi; Islam, Md Saidul; Kabir, Foysal; Khan, Ali Newaz; Khater, Ankit
In Bangladesh, the increased number of brokers has resulted in a price increase in the agricultural goods market. They acquire items from farmers at a discount and resell them to consumers at a premium. This deprives farmers, while customers pay an unreasonable price for basic essentials. This study aims to develop an e-commerce mobile and web application called 'e- Farmers' Hut' to facilitate direct producer-to-customer engagement. The application goal to maximize the advantages to producers and customers by eliminating middlemen's control. Both applications operate sequentially on the same database. Farmers and customers each have their profile to which they may submit the relevant information. Additionally, to optimize facilities use, also introduced an electronic payment method. Customers may see lists of available items that verified farmers have posted. The application has passed the testing process with expected outcomes. The technology has the ability to considerably simplify direct sales and purchases of items between farmers and consumers.