ANN for Diagnosing Hepatitis Virus

Abstract: This paper presents an artificial neural network based approach for the diagnosis of hepatitis virus. A number of factors that may possibly influence the performance of patients were outlined. Such factors as age, sex, Steroid, Antivirals, Fatigue, Malaise, Anorexia, Liver Big, Liver Firm Splean Palpable, Spiders, Ascites, Varices, Bilirubin, Alk Phosphate, SGOT, Albumin, Protine and Histology, were then used as input variables for the ANN model . Test data evaluation shows that the ANN model is able to correctly predict the diagnosis of more than 93% of prospective Patients

ANN for Predicting DNA Lung Cancer

Abstract: Lung cancer is the top reason of cancer-associated deaths globally. Surgery is the typical treatment for early-stage non-small cell lung cancer (NSCLC). Advancement in the knowledge of the biology of non-small cell lung cancer has shown molecular evidence used for systemic cancer therapy aiming metastatic disease, with a significant impact on patients’ overall survival (OS) and eminence of life. Though, a biopsy of overt metastases is an invasive technique restricted to assured positions and not effortlessly satisfactory in the clinic. The examination of peripheral blood samples of cancer patients embodies a new basis of cancer-derived material, recognized as liquid biopsy, and its constituents (circulating tumour cells (CTCS), circulating free DNA (cfDNA), exosomes, and tumour-educated platelets (TEP)) may be gotten from nearly any body liquids. These constituents have shown to imitate features of the status of both the primary and metastatic diseases, aiding the clinicians to go towards a tailored medicine. In this paper, the reasons of lung cancer will be recognized and the risk elements that initiated the increase of infection, for instance Smoking, Disclosure to secondhand smoke, Disclosure to radon gas, Disclosure to asbestos and other compounds, Family past history of lung cancer, and decrease of the spread of disease and approaches of handling and prevention of lung cancer

ANN for Parkinson’s Disease Prediction

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

Passively mode-locked laser using an entirely centred erbium-doped fiber

This paper describes the setup and experimental results for an entirely centred erbium-doped fiber laser with passively mode-locked output. The gain medium of the ring laser cavity configuration comprises a 3 m length of two-core optical fiber, wherein an undoped outer core region of 9.38 μm diameter surrounds a 4.00 μm diameter central core region doped with erbium ions at 400 ppm concentration. The generated stable soliton mode-locking output has a central wavelength of 1533 nm and pulses that yield an average output power of 0.33 mW with a pulse energy of 31.8 pJ. The pulse duration is 0.7 ps and the measured output repetition rate of 10.37 MHz corresponds to a 96.4 ns pulse spacing in the pulse train