A diagnosis system by U-net and deep neural network enabled with optimal feature selection for liver tumor detection using CT images

Rela, Munipraveena; Suryakari, Nagaraja Rao; Patil, Ramana Reddy

doi:10.1007/s11042-022-13381-2

Cited by 16 publications

(7 citation statements)

References 49 publications

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“…Performance study shows that the accuracy of the created GW-CTO-HI-DNN is 4.3%, 2.4%, 5.2%, and 4.3% better than PSO-HI-DNN, O-SHO-HI-DNN, CTO-HI-DNN, and GWO-HI-DNN. The experimental study supports the effectiveness of the created model in achieving high classification accuracy compared to other approaches [9]. Sahli et al created a method for automatically segmenting liver tumour defects from metastatic CT scans using the SegNet and U-Net architectures.…”

Section: Related Worksupporting

confidence: 55%

Convolutional Neural network-based classifiers for liver tumor detection using Computed Tomography scans

CHALLAGUNDLA,

Chintalapati,

Tunuguntla

et al. 2023

Preprint

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Automatic detection and segmentation of liver tumors from CT scans is a challenging task due to their complex morphology in CT images, but advances in deep learning have made it possible to achieve high accuracy in the segmentation and detection of liver tumors, eliminating the need for manual intervention and allowing radiologists to focus more on critical diseases by giving awareness about the liver tumor at an early stage, which may save a person's life by allowing them to receive treatment sooner, possibly preventing further severity or death. In our study, we worked on different approaches to detect liver tumors from CT scans and analyzed their performance in terms of accuracy. A few of the approaches employed are using Convolutional neural networks including existing ones like U-Net, ResNet50, while we proposed a combination of multiple CNN models to obtain better results. It was observed that the combination of CNN models produced higher accuracies rather than the existing models.

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Section: Related Worksupporting

confidence: 55%

Convolutional Neural network-based classifiers for liver tumor detection using Computed Tomography scans

CHALLAGUNDLA,

Chintalapati,

Tunuguntla

et al. 2023

Preprint

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“…When comparing with existing techniques, the proposed model achieved better performance than existing techniques such as RF, SVM, DNN-GF [ 17 ] and HI-DNN [ 21 ]. The existing techniques are implemented in our dataset and system, then results are averaged in Table 4 .…”

Section: Resultsmentioning

confidence: 98%

“…The existing technique GW-CTO [ 21 ] is implemented in our system and results are averaged in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

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Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model

et al. 2023

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Systems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same brightness and contrast characteristics as their surrounding tissues. Likewise, irregular tumour shapes are a serious concern that varies with cancer stage and tumour kind. There are two main phases of tumour segmentation in the liver: identifying the liver, and then segmenting the tumour itself. Conventional interactive segmentation approaches, however, necessitate a high number of intensity levels, whereas recently projected CNN-based interactive segmentation approaches are constrained by low presentation on liver tumour images. This research provides a unique deep Learning based Segmentation with Coot Extreme Learning Model approach that shows high efficiency in results and also detects tumours from the publicly available data of liver images. Specifically, the study processes the initial segmentation with a small number of additional users clicks to generate an improved segmentation by incorporating inner boundary points through the proposed geodesic distance encoding method. Finally, classification is carried out using an Extreme Learning Model, with the classifier’s parameters having been ideally chosen by means of the Coot Optimization algorithm (COA). On the 3D-IRCADb1 dataset, the research evaluates the segmentation quality metrics DICE and accuracy, finding improvements over approaches in together liver-coloured and tumour separation.

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“…Furthermore, a DL model was designed to detect liver tumors using an enhanced DL method called U-Net. This model combines DL algorithms and CT images resulting in a new algorithm known as Grey Wolf-Class Topper Optimization GW-CTO with a learning ability of 85% and an accuracy exceeding 90% [ 91 ]. Designing a multi-tasking AI algorithm that functions on multiple tumors is challenging.…”

Section: Deep Learning Applications In Tumor Pathologymentioning

confidence: 99%

Deep Learning Approaches in Histopathology

Ahmed

Abouzid

Kaczmarek

2022

Cancers

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The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers’ routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.

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A diagnosis system by U-net and deep neural network enabled with optimal feature selection for liver tumor detection using CT images

Cited by 16 publications

References 49 publications

Convolutional Neural network-based classifiers for liver tumor detection using Computed Tomography scans

Convolutional Neural network-based classifiers for liver tumor detection using Computed Tomography scans

Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model

Deep Learning Approaches in Histopathology

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