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Chapter and Conference Paper
Seismic Stability Analysis of Historical Construction: A Case Study - Wazirpur Tomb
The non-engineer...
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Article
Accuracy of CT chest without oral contrast for ruling out esophageal perforation using fluoroscopic esophagography as reference standard: a retrospective study
Esophageal perforation has a high mortality rate. Fluoroscopic esophagography (FE) is the procedure of choice for diagnosing esophageal perforation. However, FE can be difficult to perform in seriously ill pat...
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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) ...
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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...
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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...
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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...
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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...
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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...
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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 ...
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Article
Open AccessA hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images
We present a new approach to segment and classify bacterial spore layers from Transmission Electron Microscopy (TEM) images using a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) classifier a...
