Web Powered CT Scan Diagnosis for Brain Hemorrhage using Deep Learning

Corona virus disease (Covid - 19) has started to promptly spread worldwide from April 2020 till date, leading to massive death and loss of lives of people across various countries. In accordance to the advices of WHO, presently the diagnosis is implemented by Reverse Transcription Polymerase Chain Reaction (RT- PCR) testing, that incurs four to eight hours’ time to process test samples and adds 48 hours to categorize whether the samples are positive or negative. It is obvious that laboratory tests are time consuming and hence a speedy and prompt diagnosis of the disease is extremely needed. This can be attained through several Artificial Intelligence methodologies for prior diagnosis and tracing of corona diagnosis. Those methodologies are summarized into three categories: (i) Predicting the pandemic spread using mathematical models (ii) Empirical analysis using machine learning models to forecast the global corona transition by considering susceptible, infected and recovered rate. (iii) Utilizing deep learning architectures for corona diagnosis using the input data in the form of X-ray images and CT scan images. When X-ray and CT scan images are taken into account, supplementary data like medical signs, patient history and laboratory test results can also be considered while training the learning model and to advance the testing efficacy. Thus the proposed investigation summaries the several mathematical models, machine learning algorithms and deep learning frameworks that can be executed on the datasets to forecast the traces of COVID-19 and detect the risk factors of coronavirus.

Download Full-text

A Mask-guided Attention Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database

10.36227/techrxiv.18166667.v1 ◽

2022 ◽

Author(s):

Maede Maftouni ◽

Bo Shen ◽

Andrew Chung Chee Law ◽

Niloofar Ayoobi Yazdi ◽

Zhenyu Kong

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Imaging Modality ◽

Learning Model ◽

Classification Performance ◽

Computer Assisted ◽

Learning Approach ◽

Learning Models ◽

Task Learning ◽

Data Efficiency

The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images.The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task baseline and state-of-the-art models, as measured by various popular metrics. In our experiment with different percentages of data from our curated dataset, the classification performance gain from this multi-task learning approach is more significant for the smaller training sizes. Furthermore, experimental results demonstrate that our method enhances the focus on the lesions, as witnessed by bothattention and attribution maps, resulting in a more interpretable model.

Download Full-text

Comparison and Fusion of Deep Learning and Radiomics Features of Ground-Glass Nodules to Predict the Invasiveness Risk of Stage-I Lung Adenocarcinomas in CT Scan

Frontiers in Oncology ◽

10.3389/fonc.2020.00418 ◽

2020 ◽

Vol 10 ◽

Cited By ~ 3

Author(s):

Xianwu Xia ◽

Jing Gong ◽

Wen Hao ◽

Ting Yang ◽

Yeqing Lin ◽

...

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Stage I ◽

Ground Glass ◽

Lung Adenocarcinomas ◽

Ground Glass Nodules

Download Full-text

Number and Linear Attenuation Coefficient Variation in Hypertensive Patients with Brain Hemorrhage and Infarction Using CT Scan

Journal of Institute of Science and Technology ◽

10.3126/jist.v19i2.13866 ◽

2015 ◽

Vol 19 (2) ◽

pp. 129-134

Author(s):

Suresh Gnawali ◽

Sheskant Aryal ◽

Ananda Prasad Shreshta

Keyword(s):

Ct Scan ◽

Cortical Bone ◽

Attenuation Coefficient ◽

Linear Attenuation Coefficient ◽

Brain Hemorrhage ◽

Significant Relation ◽

Ct Number ◽

Hypertensive Patients ◽

Coefficient Variation ◽

Changing Trend

The aim of this paper is to study the variation of brain matter of Nepalese patients using CT scan. CT number and linear attenuation coefficient (LAC) of cortical bone are found largest among other brain matters, indicating the largest density. All brain matters except cortical bone show low association indicating slight changing trend with age. The cortical bone under 10 years shows low value of LAC and CT number, indicating cortical bone towards maturation whereas cortical bone shows no significant increase with age above 10 years. There is no much significant relation by gender in brain hemorrhage and infarction.Journal of Institute of Science and Technology, 2014, 19(2): 129-134

Download Full-text

Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

10.21203/rs.3.rs-65573/v1 ◽

2020 ◽

Author(s):

Yodit Abebe Ayalew ◽

Kinde Anlay Fante ◽

Mohammed Aliy

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Liver Cancer ◽

Ct Scan ◽

Tumor Segmentation ◽

Cancer Death ◽

Liver Segmentation ◽

Abdominal Ct ◽

Computed Tomography Images ◽

Abdominal Ct Scan

Abstract Background: Liver cancer is the sixth most common cancer worldwide. According to WHO data in 2017, the liver cancer death in Ethiopia reached 1040 (0.16%) from all cancer deaths. Hepatocellular carcinoma (HCC), primary liver cancer causes the death of around 700,000 people each year worldwide and this makes it the third leading cause of cancer death. HCC is occurred due to cirrhosis and hepatitis B or C viruses. Liver cancer mostly diagnosed with a computed tomography (CT) scan. But, the detection of the tumor from the CT scan image is difficult since tumors have similar intensity with nearby tissues and may have a different appearance depending on their type, state, and equipment setting. Nowadays deep learning methods have been used for the segmentation of liver and its tumor from the CT scan images and they are more efficient than those traditional methods. But, they are computationally expensive and need many labeled samples for training, which are difficult in the case of biomedical images. Results: A deep learning-based segmentation algorithm is employed for liver and tumor segmentation from abdominal CT scan images. Three separate UNet models, one for liver segmentation and the others two for tumor segmentation from the segmented liver and directly from the abdominal CT scan image were used. A dice score of 0.96 was obtained for liver segmentation. And a dice score of 0.74 and 0.63 was obtained for segmentation of tumor from the liver and from abdominal CT scan image respectively. Conclusion: The research improves the liver tumor segmentation that will help the physicians in the diagnosis and detection of liver tumors and in designing a treatment plan for the patient. And for the patient, it increases the patients’ chance of getting treatment and decrease the mortality rate due to liver cancer.

Download Full-text

A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.105014 ◽

2021 ◽

pp. 105014

Author(s):

Khabir Uddin Ahamed ◽

Manowarul Islam ◽

Ashraf Uddin ◽

Arnisha Akhter ◽

Bikash Kumar Paul ◽

...

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Chest Ct ◽

Learning Approach ◽

X Ray ◽

Chest Ct Scan

Download Full-text

Diagnosis of COVID-19 using CT scan images and deep learning techniques

10.1101/2020.07.11.20151332 ◽

2020 ◽

Author(s):

Vruddhi Shah ◽

Rinkal Keniya ◽

Akanksha Shridharani ◽

Manav Punjabi ◽

Jainam Shah ◽

...

Keyword(s):

Neural Network ◽

Computed Tomography ◽

Deep Learning ◽

Early Diagnosis ◽

Ct Scan ◽

Efficient Method ◽

Learning Models ◽

Automated Diagnosis ◽

Learning Techniques ◽

The World

Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method was based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1 %. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52 % as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening.

Download Full-text