A Survey on an Effective Identification and Analysis for Brain Tumour Diagnosis using Machine Learning Technique

The hottest issue in medicine is image analysis. It has drawn a lot of researchers since it can effectively assess the severity of the condition and forecast the outcome. The noise trimming outcomes, on the other hand, have reduced with more complex trained images, which has tended to result in a lower prediction exactness score. So, a novel Machine Learning prediction framework has been built in this present study. This work also tries to predict brain tumours and evaluate their severity using MRI brain scans. Using the boosting function, the best results for error pruning are produced. The Proposed Solution function was then used to successfully complete the feature analysis and tumour prediction operations. The intended framework is evaluated in the Python environment, and a comparative analysis is performed to examine the prediction improvement score. It was discovered that an original MLPM model had the best tumour prediction precision

Brain tumor MRI medical images classification with data augmentation by transfer learning of VGG16

The ability to estimate conclusions without direct human input in healthcare systems via computer algorithms is known as Artificial intelligence (AI) in healthcare. Deep learning (DL) approaches are already being employed or exploited for healthcare purposes, and in the case of medical images analysis, DL paradigms opened a world of opportunities. This paper describes creating a DL model based on transfer learning of VGG16 that can correctly classify MRI images as either (tumorous) or (non-tumorous). In addition, the model employed data augmentation in order to balance the dataset and increase the number of images. The dataset comes from the brain tumour classification project, which contains publicly available tumorous and non-tumorous images. The result showed that the model performed better with the augmented dataset, with its validation accuracy reaching ~100 %

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study

Background: The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC). Methods: Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning. Results: Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86-90%. Conclusions: Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles

Ensemble methods for meningitis aetiology diagnosis

In this work, we explore data-driven techniques for the fast and early diagnosis concerning the etiological origin of meningitis, more specifically with regard to differentiating between viral and bacterial meningitis. We study how machine learning can be used to predict meningitis aetiology once a patient has been diagnosed with this disease. We have a dataset of 26,228 patients described by 19 attributes, mainly about the patient's observable symptoms and the early results of the cerebrospinal fluid analysis. Using this dataset, we have explored several techniques of dataset sampling, feature selection and classification models based both on ensemble methods and on simple techniques (mainly, decision trees). Experiments with 27 classification models (19 of them involving ensemble methods) have been conducted for this paper. Our main finding is that the combination of ensemble methods with decision trees leads to the best meningitis aetiology classifiers. The best performance indicator values (precision, recall and f-measure of 89% and an AUC value of 95%) have been achieved by the synergy between bagging and NBTrees. Nonetheless, our results also suggest that the combination of ensemble methods with certain decision tree clearly improves the performance of diagnosis in comparison with those obtained with only the corresponding decision tree.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We would like to thank the Health Department of the Brazilian Government for providing the dataset and for authorizing its use in this study. We would also like to express our gratitude to the reviewers for their thoughtful comments and efforts towards improving our manuscript. Funding for open access charge: Universidad de Málaga / CBUA

An ensemble learning approach for brain cancer detection exploiting radiomic features

Background and Objective: The brain cancer is one of the most aggressive tumour: the 70% of the patients diagnosed with this malignant cancer will not survive. Early detection of brain tumours can be fundamental to increase survival rates. The brain cancers are classified into four different grades (i.e., I, II, III and IV) according to how normal or abnormal the brain cells look. The following work aims to recognize the different brain cancer grades by analysing brain magnetic resonance images. Methods: A method to identify the components of an ensemble learner is proposed. The ensemble learner is focused on the discrimination between different brain cancer grades using non invasive radiomic features. The considered radiomic features are belonging to five different groups: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. We evaluate the features effectiveness through hypothesis testing and through decision boundaries, performance analysis and calibration plots thus we select the best candidate classifiers for the ensemble learner. Results: We evaluate the proposed method with 111,205 brain magnetic resonances belonging to two freely available data-sets for research purposes. The results are encouraging: we obtain an accuracy of 99% for the benign grade I and the II, III and IV malignant brain cancer detection. Conclusion: The experimental results confirm that the ensemble learner designed with the proposed method outperforms the current state-of-the-art approaches in brain cancer grade detection starting from magnetic resonance images

Experiência profissionalizante na vertente de Investigação e Farmácia Comunitária

O presente relatório encontra-se dividido em dois capítulos: o Capítulo 1 versa sobre a vertente de Investigação e o Capítulo 2 aborda a experiência profissionalizante em Farmácia Comunitária. Relativamente ao primeiro capítulo, este é composto por uma revisão da literatura intitulada “Atividade anticancerígena do gengibre”. Esta revisão tem por base a análise de artigos científicos relacionados com o gengibre e o cancro. Na parte introdutória do capítulo é referida a importância das plantas medicinais, ao longo da história, na vida do Homem, como meio de prevenção e/ou tratamento de doenças. De seguida, são abordados os temas fitoterapia e plantas medicinais. Posteriormente, apresenta-se a história, a caracterização botânica, a composição química e as propriedades farmacológicas do gengibre (Zingiber officinale Roscoe), dando especial enfoque às suas propriedades anticancerígenas. O interesse em aprofundar o estudo destas propriedades prende-se com o facto do cancro ser uma das principais patologias do século XXI. Também é descrito a posteriori a farmacocinética e os efeitos adversos do gengibre. Por outro lado, o segundo capítulo visa descrever as atividades desenvolvidas por mim, enquanto estagiária, em Farmácia Comunitária. O estágio decorreu na Farmácia da Estação, na cidade da Guarda, entre os dias 7 de fevereiro e 17 de junho de 2022. Esta experiência permitiu-me adquirir competências práticas e consolidar conhecimentos teóricos.This report is divided into two chapters: Chapter 1 concerns the research project and Chapter 2 describes the professional experience in the area of Community Pharmacy. Regarding the first chapter, this consists of a literature review entitled "Anti-cancer activity of ginger". This review is based on the analysis of scientific articles related to ginger and cancer. In the introductory part of the chapter, the importance of medicinal plants throughout history in the scope of prevention and/or treatment of human diseases is approached. Then, the topics of phytotherapy and medicinal plants are addressed. Subsequently, the history, botanical characterisation, chemical composition and pharmacological properties of ginger (Zingiber officinale Roscoe) are presented, with a special focus on its anticancer properties. The interest in furthering the study of these properties relates to the fact that cancer is one of the major pathologies of the 21st century. The pharmacokinetics and adverse effects of ginger are also described in hindsight. On the other hand, the second chapter aims to describe the activities developed by myself, as an intern, in the area of Community Pharmacy. The internship took place at Farmácia da Estação, in the city of Guarda, between 7th February and 17th June 2022. This experience allowed me to acquire practical skills and consolidate theoretical knowledge

An Investigation of Global and Local Radiomic Features for Customized Self-Assessment Mammographic Test Sets for Radiologists in China in Comparison with Those in Australia

Self-assessment test sets have demonstrated being effective tools to improve radiologists’ diagnostic skills through immediate error feedback. Current sets use a one-size-fits-all approach in selecting challenging cases, overlooking cohort-specific weaknesses. This thesis assessed feasibility of using a comprehensive set of handcrafted global radiomic features (Stage 1, Chapter 3) as well as handcrafted (Stage 2, Chapter 4) and deep-learning based (Stage 3, Chapter 5) local radiomic features to identify challenging mammographic cases for Chinese and Australian radiologists. In the first stage, global handcrafted radiomic features and Random Forest models analyzed mammography datasets involving 36 radiologists from China and Australia independently assessing 60 dense mammographic cases. The results were used to build and evaluate models’ performance in case difficulty prediction. The second stage focused on local handcrafted radiomic features, utilizing the same dataset but extracting features from error-related local mammographic areas to analyze features linked to diagnostic errors. The final stage introduced deep learning, specifically Convolutional Neural Network (CNN), using an additional test set and radiologists’ readings to identify features linked to false positive errors. Stage 1 found that global radiomic features effectively detected false positive and false negative errors. Notably, Australian radiologists showed less predictable errors than their Chinese counterparts. Feature normalization did not improve model performance. In Stage 2, the model showed varying success rates in predicting false positives and false negatives among the two cohorts, with specific mammographic regions more prone to errors. In Stage 3, the transferred ResNet-50 architecture performed the best for both cohorts. In conclusion, the thesis affirmed the importance of radiomic features in improving curation of cohort-specific self-assessment mammography test sets