Skip to main content

and
  1. No Access

    Chapter and Conference Paper

    MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation

    A deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including ...

    Parvez Ahmad, Saqib Qamar, Linlin Shen in Brainlesion: Glioma, Multiple Sclerosis, S… (2022)

  2. No Access

    Article

    RD2A: densely connected residual networks using ASPP for brain tumor segmentation

    The variations among shapes, sizes, and locations of tumors are obstacles for accurate automatic segmentation. U-Net is a simplified approach for automatic segmentation. Generally, the convolutional or the dil...

    Parvez Ahmad, Hai **, Saqib Qamar, Ran Zheng in Multimedia Tools and Applications (2021)

  3. No Access

    Article

    Dense Encoder-Decoder–Based Architecture for Skin Lesion Segmentation

    Melanoma is one kind of dangerous cancer that has been increasing rapidly in the world. Initial diagnosis is essential to survival, but often the disease is diagnosed in the fatal stage. The rapid growth of sk...

    Saqib Qamar, Parvez Ahmad, Linlin Shen in Cognitive Computation (2021)

  4. No Access

    Chapter and Conference Paper

    HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation

    The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is criti...

    Saqib Qamar, Parvez Ahmad, Linlin Shen in Brainlesion: Glioma, Multiple Sclerosis, S… (2021)

  5. No Access

    Chapter and Conference Paper

    Context Aware 3D UNet for Brain Tumor Segmentation

    Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including br...

    Parvez Ahmad, Saqib Qamar, Linlin Shen in Brainlesion: Glioma, Multiple Sclerosis, S… (2021)

  6. No Access

    Chapter and Conference Paper

    Hybrid Labels for Brain Tumor Segmentation

    The accurate automatic segmentation of brain tumors enhances the probability of survival rate. Convolutional Neural Network (CNN) is a popular automatic approach for image evaluations. CNN provides excellent r...

    Parvez Ahmad, Saqib Qamar in Brainlesion: Glioma, Multiple Sclerosis, S… (2020)

  7. No Access

    Article

    Multi stream 3D hyper-densely connected network for multi modality isointense infant brain MRI segmentation

    Automatic accurate segmentation of medical images has significant role in computer-aided diagnosis and disease treatment. The segmentation of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) ...

    Saqib Qamar, Hai **, Ran Zheng, Parvez Ahmad in Multimedia Tools and Applications (2019)