Peer enterprises: A viable alternative to Cloud computing?

Mesothelioma is a form of cancer that is aggressive and fatal. It is a thin layer of tissue that covers the majority of the patient’s internal organs. The treatments are available; however, a cure is not attainable for the majority of patients. So, a lot of research is being done on detection of mesothelioma cancer using various different approaches; but this paper focuses on optimization techniques for optimizing the biomedical images to detect the cancer. With the restricted number of samples in the medical field, a Relief-PSO head and mesothelioma neck cancer pathological image feature selection approach is proposed. The approach reduces multilevel dimensionality. To begin, the relief technique picks different feature weights depending on the relationship between features and categories. Second, the hybrid binary particle swarm optimization (HBPSO) is suggested to automatically determine the optimum feature subset for candidate feature subsets. The technique outperforms seven other feature selection algorithms in terms of morphological feature screening, dimensionality reduction, and classification performance.

Section: Experimental Designmentioning

confidence: 99%

PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer

Praveen

Tyagi²,

Singh

et al. 2022

“…This model was proposed by a British statistician in 1972 [ 11 ], and its basic idea is to express the survival rate of patients as a risk function, that is, the probability of death of an individual in a certain unit of time during the survival process. The regression model is a semiparametric survival analysis model [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Diagnosis for Breast Cancer Patients Using Probabilistic Bayesian Classification

Junath¹,

Bharadwaj

Tyagi³

et al. 2022

The diagnosis and treatment of patients in the healthcare industry are greatly aided by data analytics. Massive amounts of data should be handled using machine learning approaches to provide tools for prediction and categorization to support practitioner decision-making. Based on the kind of tumor, disorders like breast cancer can be categorized. The difficulties associated with evaluating vast amounts of data should be overcome by discovering an efficient method for categorization. Based on the Bayesian method, we analyzed the influence of clinic pathological indicators on the prognosis and survival rate of breast cancer patients and compared the local resection value directly using the lymph node ratio (LNR) and the overall value using the LNR differences in effect between estimates. Logistic regression was used to estimate the overall LNR of patients. After that, a probabilistic Bayesian classifier-based dynamic regression model for prognosis analysis is built to capture the dynamic effect of multiple clinic pathological markers on patient prognosis. The dynamic regression model employing the total estimated value of LNR had the best fitting impact on the data, according to the simulation findings. In comparison to other models, this model has the greatest overall survival forecast accuracy. These prognostic techniques shed light on the nodal survival and status particular to the patient. Additionally, the framework is flexible and may be used with various cancer types and datasets.

“…Literature [ 10 ] employed a fully convolutional neural network with an encoder-decoder to segment the MR images of 27 NPC patients and used leave-one-out cross-validation to accomplish NPC segmentation. Literature [ 11 ] also employed a convolutional neural network to separate the lesion region of 30 NPC patients' T1W modality MR data. To enhance the segmentation findings, they applied a 3D graph cut technique.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification

Martin

Sharma

Kaur

et al. 2022

Weighted MR images of 421 patients with nasopharyngeal cancer were obtained at the head and neck level, and the tumors in the images were assessed by two expert doctors. 346 patients’ multimodal pictures and labels served as training sets, whereas the remaining 75 patients’ multimodal images and labels served as independent test sets. Convolutional neural network (CNN) for modal multidimensional information fusion and multimodal multidimensional information fusion (MMMDF) was used. The three models’ performance is compared, and the findings reveal that the multimodal multidimensional fusion model performs best, while the two-modal multidimensional information fusion model performs second. The single-modal multidimensional information fusion model has the poorest performance. In MR images of nasopharyngeal cancer, a convolutional network can precisely and efficiently segment tumors.