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Browsing Project Report by Subject "Medical Imaging"

Browsing Project Report by Subject "Medical Imaging"

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  • Automated Detection Leukemia Subtype Classification from Microscopic Images: A Convolutional Neural Network Approach 
    Lubna, Sheikh Bodrun Nesa (Daffodil International University, 2024-01-25)
    Leukemia is a difficult type of blood cancer which can present in a combination of multiple types, that had individual cellular and genetic abnormalities. The prevalent forms of leukemia include Acute lymphocytic leukemia ...
  • Brain hemorrhage recognition using deep learning approach 
    Shimanto, Sakib Shahariar (Daffodil International University, 2024-01-22)
    This study addresses the use of deep learning techniques, including Inception, ResNet, Xception, AlexNet, and MobileNet, for the identification of brain hemorrhages in medical imaging. Motivated by the crucial need for ...
  • Brain hemorrhage recognition using deep learning approach 
    Sakib Shahariar Shimanto (Daffodil International University, 2024-01-22)
    This study addresses the use of deep learning techniques, including Inception, ResNet, Xception, AlexNet, and MobileNet, for the identification of brain hemorrhages in medical imaging. Motivated by the crucial need for ...
  • Breast Tumor detection using deep learning 
    Atikuzzaman, Md. (Daffodil International University, 2024-01-25)
    Finding tumor areas on breast ultrasonography pictures has long been a fascinating subject. The complex architecture of breast and the presence of noise in ultrasound images sometimes make it impossible for traditional ...
  • Classification of Bone fractures using X-Ray images with the help of deep learning 
    Rahman, Mizanur (Daffodil International University, 2024-01-24)
    An established paradigm in the field of imaging in medical is fracture detection. These days, computer-aided diagnostic-(CAD) systems are widely used because they help physicians and other medical experts diagnose various ...
  • Contrastive learning approaches for ophthalmic biomarker identification: unveiling insights into eye health 
    Chamok, Farjana Haque (Daffodil International University, 2024-01-01)
    The field of ophthalmic biomarker identification plays a pivotal role in understanding and monitoring eye health. In this study, I leverage the EfficientViT_m5.r224_inlk model as our foundational framework to explore ...
  • A deep convolutional neural networks for brain tumor classification based on MRI images 
    Al Mamun, Shamim (Daffodil International University, 2024-01-26)
    The tenth most common cause of death for both men and women is cancer of thebrain and associated nervous system tumors. According to estimates, primarymalignant brain and central nervous system tumors will be the cause of ...
  • Enhancing brain tumor detection with royal filter and btv19 model 
    Hossain, Sadia (Daffodil International University, 2024-01-01)
    This work aims to enhance brain tumor diagnosis accuracy by examining crucial aspects of cleaning and filtering MRI datasets, emphasizing the novel integration of Royal filtering with the VGG19 architecture. It employs ...
  • Enhancing diagnostic precision: machine learning for blood cell image classification 
    Al Maruf, Abdullah (Daffodil International University, 2024-01-25)
    The accurate and timely analysis of blood cell images plays a crucial role in disease diagnosis and monitoring. This research looks into the segmentation and classification of white blood cells using machine learning ...
  • Graph based Automatic Breast Tumor Classification Through Ultrasound Images by Radiomics Features 
    Hossain, Md. Shakhawat (Daffodil International University, 2024-01-24)
    Breast tumor a common and devastating cancer in women, poses significant challenges in both diagnosis and prognostication. In this study, we aim to enhance the classification of breast tumor by breast ultrasound image ...
  • Skin disease classification using deep learning 
    Remel, Mahinoor Islam (Daffodil International University, 2024-01-01)
    This study presents an in-depth study of the development of Skin Disease Classification using Deep Learning. The dataset was carefully selected to ensure that it was representative of the skin Disease of various kinds: ...
  • Skin disease classification using deep learning 
    Remel, Mahinoor Islam (Daffodil International University, 2024-01-24)
    This study presents an in-depth study of the development of Skin Disease Classification using Deep Learning. The dataset was carefully selected to ensure that it was representative of the skin Disease of various kinds: ...
  • Thesis on Lung and Colon Cancer Prediction and detection using Deep Learning 
    Dibosh, Arnob Ghosh (Daffodil International University, 2024-01-01)
    Classification of medical images is essential to the diagnosis of many diseases. This paper presents a thorough analysis of multimodal deep learning fusion methods for problems involving the classification of medical ...
  • Thesis on Lung and Colon Cancer prediction and detection using deep learning 
    Dibosh, Arnob Ghosh (Daffodil International University, 2024-01-25)
    Classification of medical images is essential to the diagnosis of many diseases. This paper presents a thorough analysis of multimodal deep learning fusion methods for problems involving the classification of medical ...
  • Web based classification of brain tumor using deep learning 
    Al Noman, Abdullah (Daffodil International University, 2024-01-22)
    Classification of brain tumors is one of the most crucial jobs in medical imaging, and deep learning models have shown promising outcomes when it comes to automation. We provide a thorough analysis of three deep learning ...
  • Web-based classification of brain tumor using deep learning 
    Al Noman, Abdullah (Daffodil International University, 2024-01-01)
    Classification of brain tumors is one of the most crucial jobs in medical imaging, and deep learning models have shown promising outcomes when it comes to automation. We provide a thorough analysis of three deep learning ...

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