Now showing items 1-15 of 15

    • Automated detection of Malignant Lesions in the ovary using deep learning models and XAI 

      Ifty, Md. Hasin Sarwar; Nirjan, Nisharga; Diganta, M.A.; Islam, Labib; Ornate, Reeyad Ahmed (Brac University, 2024-01)
      Cancer is a complex and highly invasive disease that forms due to the abnormal growth of cells in any part of the body. A majority of cancers are unraveled and treated by incorporating advanced technology. However, ovarian ...
    • Classification of potato and corn leaf diseases using deep learning 

      Mahbub, Sheikh Alima; Siddique, Mayisha; Hasan, Tasmia; Tasmeem, Nazia; Proma, Rubaba Aziz (Brac University, 2024-06)
      One of the major hindrances to sustainable agriculture and an imminent threat to food security is plant disease. Constantly monitoring a plant’s health and spotting the problems in it is quite painstaking because it ...
    • Deep learning based early Glaucoma detection 

      Islam, Aabrar; Haque, Ayen Aziza; Tasnim, Najifa; Waliza, Simin (Brac University, 2024-01)
      Glaucoma is a severe eye condition that can lead to progressive vision impairment if left untreated. Diagnosis and monitoring of glaucoma at an initial stage is critical for effective treatment of the disease. However, ...
    • Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification 

      Datta, Nirjhor; Rashid, Md. Hasanur; Rahman, Samiur; Nodi, Naima Tahsin; Uddin, Moin (Brac University, 2024-01)
      Adrenocortical Carcinoma (ACC) is a rare but highly lethal cancer that occurs in the adrenal cortex. Accurate diagnosis of ACC are vital in order to determine appropriate treatment strategies and predict patient outcomes. ...
    • Detecting different stages of Alzheimer’s disease from MRI images using deep learning and computer vision techniques 

      Rahman, Fardin; Sharif, Sadman; Islam, Syed Shams; Tirtho, Nihad Adnan Shah; Intheshar, Md. Ashir (Brac University, 2024-01)
      The preliminary and precise diagnosis of Alzheimer’s Disease is significant for the speedy management and intervention of the disorder. Numerous valuable tools such as Magnetic Resonance Imaging (MRI), Positron Emission ...
    • Disease detection system of mango leaves 

      Islam, Sabrina; Paul, Apurba; Chowdhury, Oishe Roy; Fiza, Fahmida Akhter (Brac University, 2023-12)
      This work aims to design and implement a disease detection system for mango leaves. We have proposed two design approaches for the disease detection system for mango leaves. From these two design approaches, we have ...
    • Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs 

      Alvi, Md.Waseq Alauddin (Brac University, 2024-05)
      Pneumonia, a known leading child killer and a general health burden, continues to be a major concern due to its high morbidity and mortality rates in the developing world, which calls for prompt and accurate diagnosis. ...
    • Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture 

      Radiah, Faiza; Rahman, Kabasum; Asadullah, Lasania; Sohan, Md. Sohanur Rahman; Ahmed, Jaki (Brac University, 2023-09)
      Skin Cancer is a cancer form that has become very prevalent in recent times and, if left untreated, has the potential to cause premature death. That is why early diagnosis and treatment are important to cure this disease. ...
    • Exploring machine learning techniques for symptom-based detection of livestock diseases 

      Niloy, Mahir Ahmed; Bhowmik, Tanmay; Abedin, Jennifer; Ferdous, Syeda Jannatul; Jahan, Ishrat (Brac University, 2024-05)
      Effective livestock monitoring ensures food security and sustainability in our rapidly growing world. However, proper cattle disease is still not taken seriously in our country. Even in the livestock industry, it has not ...
    • An interpretable diagnosis of retinal diseases using vision transformer and Grad-CAM 

      Bhuiyan, Mahdi Hasan; Haldar, Sumit; Chowdhury, Maisha Shabnam; Bushra, Nazifa; Jilan, Tahsin Zaman (Brac University, 2024-01)
      Early detection of retinal diseases can help people avoid going completely or partially blind. In this research, we will be implementing an interpretable diagnosis of retinal diseases using a hybrid model containing ...
    • Mango leaf disease detection using image processing 

      Sarkar, Avizit; Hasan, Murshed; Sarker, Nirnoy Chandra; Srabon, Moin Nadim; Sufia, Safwat (Brac University, 2024)
      Bangladesh is an agricultural country and mango cultivation plays a significant role in the economy of Bangladesh. Mango trees are at risk of different kinds of leaf disease. As a result, it can be the reason for hindering ...
    • PlantGuard: intelligent plant disease detection 

      Khanom, Nazifa (Brac University, 2024-05)
      Every year, there is significant crop loss in developing countries due to delays in identifying plant diseases. Prompt and accurate identification of these diseases, with less reliance on field experts, could greatly ...
    • A semi-supervised federated learning approach leveraging pseudo-labeling for Knee Osteoarthritis severity detection 

      Rifat, Rakib Hossain (Brac University, 2024-06)
      Within medical image analysis, appropriately classifying the extent of knee osteoarthritis is a significant obstacle, made more difficult by the scarcity of annotated data and strict privacy rules. Conventional approaches ...
    • Skin cancer classification for seven types of skin lesions 

      Rahman, Md. Tawsifur; Azad, Md. Siam Sadman; Muhtasim, Ali (Brac University, 2023-05)
      Machine learning (ML) for skin lesion identification employs algorithms, notably convolutional neural networks (CNNs), to categorize and detect skin lesions, aiming to enhance early detection and treatment of skin cancer. ...
    • Smart detection and classification of fungal disease in rice plants using image processing techniques 

      Rashed, Akib; Ifraj, Sabista; Toa, Mashfia Zaman (Brac University, 2024-05)
      One of the most crucial staple crops, rice (Oryza Sativa), feeds a significant percentage of the world’s population. However, fungal infections, which may significantly reduce yields and affect global food security, ...