Browsing by Author "Hasan, MD. Zahid"

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Design and Implementation of a Smart Hospital Management System
(Daffodil International University, 23-03-01) Hasan, MD. Zahid; Hossain, MD. Mobarok
This project Smart Hospital Management Application includes registration of patients, storing their details into the system, and also computerized billing in the pharmacy, and labs. The software has the facility to give a unique id for every patient and stores the clinical details of every patient and hospital tests done automatically. It includes a search facility to know the current status of each patient. User can search details of a patient using the id. The Hospital Management System can be entered using a username and password. It is accessible either by an administrator or receptionist. Only they can add data into the database. The data can be retrieved easily. The interface is very user-friendly. The data are well protected for personal use and makes the data processing very fast.
Exploring the efficiency of transfer learning in Brinjal disease detection using deep learning
(DAFFODIL INTERNATIONAL UNIVERSITY, 2024-09-01) Hasan, MD. Zahid
About 75% of the people in Asian countries rely on agriculture for their livelihood. Bangladesh is a country highly dependent on agriculture [1]. About 45.33% of the population of the country was engaged in the agriculture sector in the fiscal year 2022–2023 [2]. And same sector contributed approximately 11.38% to the Gross Domestic Product of the nation [3]. Grown over 50,000 hectares of land, eggplant, or brinjal, is the third most important crop in the nation [4]. This vegetable is very beneficial to health as it aids in better digestion and increases mental performance. It is also known to prevent one from catching cancer, protects heart health, and also supports the bones in one's body. Antioxidants, including vitamins A and C, which protect cells from damage, are very much present in brinjal. It has a high concentration of polyphenols, which are chemical molecules that help control how sugar is metabolized in diabetic cells
Time Distributed -CNN-LSTM
(Daffodil International University, 2022-06-10) Montaha, Sidratul; Azam, Sami; Rafid, A. K. M. Rakibul Haque; Hasan, MD. Zahid; Karim, Asif; Islam, Ashraful
Identification of brain tumors at an early stage is crucial in cancer diagnosis, as a timely diagnosis can increase the chances of survival. Considering the challenges of tumor biopsies, three dimensional (3D) Magnetic Resonance Imaging (MRI) are extensively used in analyzing brain tumors using deep learning. In this study, three BraTS datasets are employed to classify brain tumor into two classes where each of the datasets contains four 3D MRI sequences for a single patient. This research is composed of two approaches. In the first part, we propose a hybrid model named TimeDistributed-CNN-LSTM (TD- CNN-LSTM) combining 3D Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) where each layer is wrapped with a TimeDistributed function. The objective is to consider all the four MRI sequences of each patient as a single input data because every sequence contains necessary information of tumor. Therefore, the model is developed with optimal configuration performing ablation study for layer architecture and hyper-parameters. In the second part, a 3D CNN model is trained respectively with each of the MRI sequences to compare the performance. Moreover, the datasets are preprocessed to ensure highest performance. Results demonstrate that the TD-CNN-LSTM network outperforms 3D CNN achieving the highest test accuracy of 98.90%. Later, to evaluate the performance consistency, the TD-CNN-LSTM model is evaluated with K-fold cross validation. The approach of putting together all the MRI sequences at a time with good generalization capability can be used in future medical research which can aid radiologists in tumor diagnostics effectively.