Automatic Segmentation of Calcification Areas in Digital Breast Images.

In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment.

Discovery of Knowledge in the Incidence of a Type of Lung Cancer for Patients through Data Mining Models.

This paper presents the research results on the contribution of user-centered data mining based on the standard principles, focusing on the analysis of survival and mortality of lung cancer cases. Researchers used anonymized data from previously diagnosed instances in the health database to predict the condition of new patients who have not had their results yet. Medical professionals specializing in this field provided feedback on the usefulness of the new software, which was constructed using WEKA data mining tools and the Naive Bayes method. The results of this article provide elements of interest to discuss the value of identifying or discovering relationships in apparently "hidden" information to propose strategies to counteract health problems or prevent future complications and thus contribute to improving the quality of care. Life of the population, as would be the case of data mining in the health area, has shown applicability in the early detection and prevention of diseases for the analysis of genetic markers to determine the probability of a satisfactory response to medical treatment, and the most accurate model was Naive Bayes (91.1%). The Naive Bayes algorithm's closest competitor, bagging, came in second with 90.8%. The analysis found that the ZeroR algorithm had the lowest success rate at 80%.

Early Detection of Autism Spectrum Disorders (ASD) with the Help of Data Mining Tools.

Autism is a disorder of neurobiological origin that originates a different course in the development of verbal and nonverbal communication, social interactions, the flexibility of behavior, and interests. The results obtained offer relevant information to reflect on the practices currently used in assessing the development of children and the detection of ASD and suggest the need to strengthen the training of health professionals in aspects such as psychology and developmental disorders. This study, based on genuine and current facts, used data from 292 children with an autism spectrum disorder. The input dataset has 20 characteristics, and the output dataset has one attribute. The output property indicates whether or not a certain person has autism. The research study first and foremost performed data pretreatment activities such as filling in missing data gaps in the data collection, digitizing categorical data, and normalizing. The features were then clustered using k-means and x-means clustering methods, then artificial neural networks and a linguistic strong neurofuzzy classifier were used to classify them. The outcomes of each strategy were examined, and their respective performances were compared.

Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care.

Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the denomination of methods used to create visual representations of the human body for clinical analysis, such as diagnosing, monitoring, and treating medical conditions. In this research, a computational proposal is presented to aid the diagnosis of colon cancer, which consists of using hyperspectral images obtained from slides with biopsy samples of colon tissue in paraffin, characterizing pixels so that, afterwards, imaging techniques can be applied. Using computer graphics augmenting conventional histological deep learning architecture, it can classify pixels in hyperspectral images as cancerous, inflammatory, or healthy. It is possible to find connections between histochemical characteristics and the absorbance of tissue under various conditions using infrared photons at various frequencies in hyperspectral imaging (HSI). Deep learning techniques were used to construct and implement a predictor to detect anomalies, as well as to develop a computer interface to assist pathologists in the diagnosis of colon cancer. An infrared absorbance spectrum of each of the pixels used in the developed classifier resulted in an accuracy level of 94% for these three classes.

Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams.

Schistosoma mansoni is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image-based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. . However, the SPNN proved to be faster than the CNN.

Sleep-wake cycle disturbances in protein-energy malnutrition: effect of nutritional rehabilitation.

A standard sleep questionnaire was given to the parents of 26 infants with protein-energy malnutrition who underwent polysomnographic evaluation. These investigations were repeated approximately 2 months after enrolment in a nutritional rehabilitation programme based on World Health Organization guidelines. Anthropometric values and serum serotonin levels were also measured. After nutritional rehabilitation there was a significantly higher percentage of non-rapid eye movement (REM) sleep; 2nd REM time, and latency times for sleep and REM sleep increased. Percentages of REM sleep and serum serotonin levels decreased significantly. Protein-energy malnutrition seems to affect the sleep-wake cycle; disturbed serotonin levels may be among the factors responsible.

Sleep-wake cycle disturbances in protein-energy malnutrition: effect of nutritional rehabilitation

A standard sleep questionnaire was given to the parents of 26 infants with protein-energy malnutrition who underwent polysomnographic evaluation. These investigations were repeated approximately 2 months after enrolment in a nutritional rehabilitation programme based on World Health Organization guidelines. Anthropometric values and serum serotonin levels were also measured. After nutritional rehabilitation there was a significantly higher percentage of non-rapid eye movement [REM] sleep; 2nd REM time, and latency times for sleep and REM sleep increased. Percentages of REM sleep and serum serotonin levels decreased significantly. Protein-energy malnutrition seems to affect the sleep-wake cycle; disturbed serotonin levels may be among the factors responsible

Short term use of intracavernous vasoactive drugs in the treatment of persistent psychogenic erectile dysfunction.

The objective of this retrospective study is to evaluate the effectiveness of short term intracavernous pharmacotherapy in the treatment of persistent psychogenic impotence. The study included 153 patients evaluated within an Andrology clinic in a general hospital working in close relation to three psychiatry clinics in three hospitals. Patients underwent an average of two office sessions of intracavernous injections for the selection of the appropriate intracavernous agent and dose, and for self injection training. Of the 153 patients included in our study 98 (64%) needed the injection for less than three months, only 18 (12%) patients needed the injections up to one year. We concluded that intracavernous self injection of vasoactive drugs is an effective alternative in the treatment of men with persistent psychogenic impotence when sex therapy is unsuccessful.