Detection of Liver Cancer through Computed Tomography Images using Deep Convolutional Neural Networks

Naaqvi, Zunaira; Akbar, Shahzad; Hassan, Syed Ale; Ain, Qurat Ul

doi:10.1109/icodt255437.2022.9787429

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…This technique is primarily based on the model education through pattern sample (photograph, video, signals, or textual content) as an input. [10].…”

Section: With Data Collectionmentioning

confidence: 99%

“…Before segmentation to take place, system need right edges from the image. Based on the boundary and the differences in the colour level or colour shades system detects the area or segment the area in image [10]. This segmentation has multiple input cluster size as shown in result figure 5.…”

Section: Figure 4: Image Colour Pallet Generationmentioning

confidence: 99%

“…Also, shape and size of liver differs from person to person. It is different even for male and female [6]. The liver cancer causes structural, shape and intensity changes in the liver images which can be used to distinguish between normal and cancerous liver image [7].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

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Cancer Symptoms Detection from Liver CT Images Using Multistage Pre-Processors

Siddique

Singh

Hasan³

2023

IJEER

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Visually cancer is the abnormal pattern with predefined structure could be found in liver Computed Tomography (CT) images. Using deep convolution neural network computation and image processing, this detected abnormal pattern cluster can be classified in different liver issue types. Full size liver CT scan images consisting different body parts, and these are ultrasonic based gray scaled image construction. The primary challenge in the cancer symptoms detection process is to extract the liver area out of image then finding out the actual area of abnormality to conclude whether abnormality is cancer or any other issues on liver. This is two stage processes, first is to segment the abnormality area and second is to perform pattern matching to identify the abnormality. This research paper primarily focuses on different pre-processing techniques and stages involved in liver abnormality segmentation.

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“…This technique is primarily based on the model education through pattern sample (photograph, video, signals, or textual content) as an input. [10].…”

Section: With Data Collectionmentioning

confidence: 99%

Section: Figure 4: Image Colour Pallet Generationmentioning

confidence: 99%

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Cancer Symptoms Detection from Liver CT Images Using Multistage Pre-Processors

Siddique

Singh

Hasan³

2023

IJEER

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“…27 In the medical field, artificial intelligence [28][29][30] is explored in a variety of ways. Deep learning helps in many areas of research for the automated detection of different diseases such as hypertensive retinopathy, 31,32 brain tumor, [33][34][35][36] papilledema, 37,38 glaucoma, [39][40][41] melanoma, 42,43 alzheimer disease, [44][45][46][47] central serous retinopathy, 48 leukemia, 49,50 and liver cancer 51 in healthcare. Among DL techniques, the neural network has acquired a lot of popularity and is widely used in image classification.…”

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confidence: 99%

A deep learning approach for multi‐stage classification of brain tumor through magnetic resonance images

Gull

Akbar

Naqi

2023

Int J Imaging Syst Tech

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Brain tumor is the 10th major cause of death among humans. The detection of brain tumor is a significant process in the medical field. Therefore, the objective of this research work is to propose a fully automated deep learning framework for multistage classification. Besides, this study focuses on to develop an efficient and reliable system using a convolutional neural network (CNN). In this study, the fast bounding box technique is used for segmentation. Moreover, the CNN layers‐based three models are developed for multistage classification through magnetic resonance images on three publicly available datasets. The first dataset is obtained from Kaggle Repository (Dataset‐1), the second dataset is known as Figshare (Dataset‐2), and the third dataset is called REMBRANDT (Dataset‐3) to classify the MR images into different grades. Different augmentation techniques are applied to increase the data size of MR images. In pre‐processing, the proposed models achieved higher Peak Signal‐to‐Noise ratio to remove noise. The first proposed deep CNN framework mentioned as Classification‐1 has obtained 99.40% accuracy, which classified MR images into two classes, that is (i) normal and (ii) abnormal, while the second proposed CNN framework mentioned as Classification‐2 has obtained 97.78% accuracy, which classified brain tumor into three types, which are meningioma, glioma, and pituitary. Similarly, the third developed CNN framework mentioned as Classification‐3 has obtained 98.91% accuracy that further classified MR images of tumors into four different classes as: Grade I, Grade II, Grade III, and Grade IV. The results demonstrate that the proposed models achieved better performance on three large and diverse datasets. The comparison of obtained outcomes shows that the developed models are more efficient and effective than state‐of‐the‐art methods.

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