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Deep Learning for Medical Dataset Classification Based on Convolutional Neural Networks
Published in R. Sujatha, S. L. Aarthy, R. Vettriselvan, Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics, 2021
These days, clinical indicative sweeps, for instance, Computed Tomography (CT) and Magnetic Reverberation Imaging (MRI) are significant for the analysis and evaluation of treatment for numerous illnesses. Liver tumor (malignancy) is the most well-known tumor illness around the world, and it prompts critical fatalities on a yearly basis. Exact tumor estimations (from MRI and CT), as well as tumor size, area, and shape, can help specialists in making exact disease evaluation and treatment arrangements. The programmed division of liver and tumor faces numerous difficulties including the difference level among liver and tumor is moderately little, there are fluctuating sizes and kinds of liver tumors and abnormalities in tissues. Most Deep Learning analysts accept that the more profound model is the best. By and by, the more profound model faces popping/disappearing slope issues, which impede combination through preparing (Moghbel et al., 2018).
Poly(Alkyl Cyanoacrylate) Nanoparticles for Delivery of Anti-Cancer Drugs
Published in Mansoor M. Amiji, Nanotechnology for Cancer Therapy, 2006
R. S. R. Murthy, L. Harivardhan Reddy
HCC is the most common liver tumor, with heterogeneity in the tumor behavior and the underlying liver disease. Recent combinations such as cisplatin, interferon, Adriamycin, and 5-FU are extremely toxic and yield response rates of only 20%, with no survival advantage compared to supportive care alone.184 Higher concentrations of cancer chemotherapeutic agents can be delivered directly to the HCC via the hepatic arterial route. Considering that this route is the major vascular supply of these tumors, an even larger number of papers have reported the experience of hepatic artery chemotherapy or hepatic artery chemoembolization (TACE) with single agents, or with a dizzying combination of agents, and at doses not replicated by any two institutions. Loewe et al.185 evaluated the potential of transarterial permanent embolization with the use of a mixture of cyanoacrylate and lipiodol for the treatment of unresectable primary HCC. Loewe et al.186 used NBCA for hepatic artery embolization for the treatment of small-bowel neuroendocrine metastases to the liver. The results revealed that the permanent embolization of hepatic arteries as part of a multimodality treatment protocol is beneficial in long-term follow-up for patients with metastasized small-bowel neuroendocrine tumors. The use of cyanoacrylate is safe and effective as an embolic agent.
Toxicology
Published in Samuel C. Morris, Cancer Risk Assessment, 2020
An example introduced earlier is the B6C3F1 hybrid mouse, the standard mouse used in the NTP bioassay. This mouse has a high spontaneous liver tumor rate. The question has been raised of whether such an increase in liver tumors has any implication for human carcinogenicity. As of June, 1984, 50% of all agents found to be carcinogenic on NTP and NCI bioassays gave positive responses in the mouse liver (Maronpot, in press). More than 28 compounds have been identified for which the mouse liver was the only site with increased incidence of tumors. Epidemiological evidence strongly suggests that at least one of these agents (phenobarbi-tal), is not a human carcinogen Clayson (1987).
Computer Vision Approach for Liver Tumor Classification Using CT Dataset
Published in Applied Artificial Intelligence, 2022
Mubasher Hussain, Najia Saher, Salman Qadri
This study focused on classification of liver tumors into a benign (hemangioma, cyst) and malignant (hepatocellular carcinoma, metastasis). The dataset consists of CT images of liver obtained from Nishter Medical University, Multan, Pakistan. Multi-featured were extracted from different ROIs segments. Features were optimized using the CFS technique. Four ML classifiers, namely, J48, LMT, RF, and RT were deployed on ROIs size , , , , and . ML classifiers produced better accuracy on , , ROIs size. Among four ML classifiers, RF and RT produced promising results 97.48% and 97.08% on ROI respectively. The variation of results in different classifiers was due to the modalities of the dataset.
Iterative Convolutional Encoder-Decoder Network with Multi-Scale Context Learning for Liver Segmentation
Published in Applied Artificial Intelligence, 2022
Feiyan Zhang, Shuhao Yan, Yizhong Zhao, Yuan Gao, Zhi Li, Xuesong Lu
Liver segmentation on medical images plays a critical role in hepatic disease diagnosis, function assessment, radiotherapy planning, and image-guided surgery. In clinical workflow, computed tomography (CT) is the most common technique for detecting numerous types of malignant liver tumors (Chen et al. 2011). On the other hand, due to non-ionizing radiation and better contrast of soft tissues, magnetic resonance (MR) imaging is increasingly used to monitor liver volume and fat content, which could aid in reducing the need of more invasive biopsies (Tang et al. 2015).