Brain Tumor Detection Using MRI Images

When abnormal cells develop within the brain, a tumor is formed. Early tumor detection improves the likelihood of a patient\u27s recovery. Compared to CT scan pictures, magnetic resonance imaging (MRI) is a trustworthy method for finding malignancies. In this project, we will use deep learning methods to detect tumors faster with higher accuracy using MRI images. Specifically, we will investigate the performance of transfer learning models based on convolutional neural networks (CNN) structures on the tumor detection problem. A machine learning approach called transfer learning uses a model already trained for the present task. The advantage of this technique is that we do not need to train the model from scratch, which will save time and increase accuracy. With the help of the Visual Geometry Group (VGG 16), Inception V3, and Resnet 50, this study attempts to identify brain tumors. It also uses a methodical approach for hyperparameter tuning to improve the trained models\u27 accuracy. The main objective is to develop a practical approach for detecting brain tumors using MRIs to make quick, efficient, and precise decisions regarding the patients\u27 conditions. Our suggested methodology is evaluated on the Kaggle dataset, taken from BRATS 2015 for brain tumor diagnosis using MRI images, including 3700 MRI brain images, with 3300 showing tumors. The simulation results show that training the deep learning models could achieve an accuracy of 96.0% for VGG-16, 94% for Resnet50, and 90.7% for the InceptionV3 model. In order to improve the accuracy even further, Bayesian Optimization is leveraged as a hyperparameter tuning technique to obtain the best set of parameters. We could achieve the accuracy of 97.5% for VGG-16, 95% for Resnet50, and 91.5% for InceptionV3

A Comparative Study of Two State-of-the-Art Feature Selection Algorithms for Texture-Based Pixel-Labeling Task of Ancient Documents

International audienceRecently, texture features have been widely used for historical document image analysis. However, few studies have focused exclusively on feature selection algorithms for historical document image analysis. Indeed, an important need has emerged to use a feature selection algorithm in data mining and machine learning tasks, since it helps to reduce the data dimensionality and to increase the algorithm performance such as a pixel classification algorithm. Therefore, in this paper we propose a comparative study of two conventional feature selection algorithms, genetic algorithm and ReliefF algorithm, using a classical pixel-labeling scheme based on analyzing and selecting texture features. The two assessed feature selection algorithms in this study have been applied on a training set of the HBR dataset in order to deduce the most selected texture features of each analyzed texture-based feature set. The evaluated feature sets in this study consist of numerous state-of-the-art texture features (Tamura, local binary patterns, gray-level run-length matrix, auto-correlation function, gray-level co-occurrence matrix, Gabor filters, Three-level Haar wavelet transform, three-level wavelet transform using 3-tap Daubechies filter and three-level wavelet transform using 4-tap Daubechies filter). In our experiments, a public corpus of historical document images provided in the context of the historical book recognition contest (HBR2013 dataset: PRImA, Salford, UK) has been used. Qualitative and numerical experiments are given in this study in order to provide a set of comprehensive guidelines on the strengths and the weaknesses of each assessed feature selection algorithm according to the used texture feature set

Prototipo CAD de segmentación automática de cáncer de pulmón en imágenes histopatológicas TMA

El cáncer de pulmón es una enfermedad letal que para el 2012 se situó como la quinta causa de muerte a nivel mundial, la tercera en Europa y la primera en España con casi 20.000 nuevos casos cada año; aproximadamente el 85 % de los sujetos que padecen cáncer de pulmón, morirán por esta enfermedad. El principal obstáculo en la lucha contra esta patología es su detección tardía. El desarrollo que ha experimentado el campo de la imagen médica en aspectos como la adquisición, almacenamiento y visualización ha contribuido al mejoramiento de la calidad del análisis y diagnóstico de las diferentes patologías (entre ellas el cáncer de pulmón) convirtiéndola actualmente en un componente indispensable en medicina. En las últimas décadas, se han realizado numerosos esfuerzos para detectar de manera precoz el cáncer de pulmón mediante el desarrollo de distintas tecnologías, entre ellas los sistemas de diagnóstico asistido por computador (CAD), los cuales mediante el análisis automático de la imagen médica brindan al especialista una segunda opinión diagnostica, con el objetivo de obtener diagnósticos mas precisos que permitan formular tratamientos mas adecuados. La imagen médica histopatológica es el "gold standard. en detección temprana de la mayoría de patológicas incluido el cáncer de pulmón. La tarea de detección suele ser bastante tediosa e que implica una importante inversión de tiempo y esfuerzo por parte de los expertos en histopatología. El crecimiento de los bancos de tejidos ya ha superado las habilidades manuales de análisis disponibles. Además, la revisión de patología experta sufre variaciones ínter e intra observador. Lo anterior evidencia la gran necesidad de automatizar el análisis de imagen médica en histopatológica. En este trabajo se hace una aproximación a la detección de cáncer de pulmón en imagen médica, concretamente abordando el problema de segmentación de tejido tumoral y no tumoral sobre imágenes histopatológicas TMA, mediante el desarrollo de un prototipo de sistema de diagnóstico asistido por computador CAD

Texture feature benchmarking and evaluation for historical document image analysis

International audienceThe use of different texture-based methods is pervasive in different sub-fields and tasks of document image analysis and particularly in historical document image analysis. Nevertheless, faced with a large diversity of texture-based methods used for historical document image analysis, few questions arise. Which texture methods are firstly well suited for segmenting graphical contents from textual ones, discriminating various text fonts and scales, and separating different types of graphics? Then, which texture-based method represents a constructive compromise between the performance and the computational cost? Thus, in this article a benchmarking of the most classical and widely used texture-based feature sets has been conducted using a classical texture-based pixel-labeling scheme on a large corpus of historical documents to have satisfactory and clear answers to the above questions. We focus on determining the performance of each texture-based feature set according to the document content. The results reported in this study provide firstly a qualitative measure of which texture-based feature sets are the most appropriate, and secondly a useful benchmark in terms of performance and computational cost for current and future research efforts in historical document image analysis