A Comparative Study for Methodologies and Algorithms Used In Colon Cancer Diagnoses and Detection

Colon cancer is also referred to as colorectal cancer; it is a kind of cancer that starts with colon damage to the large intestine in the last section of the digestive tract. Elderly people typically suffer from colon cancer, but this may occur at any age. It normally starts as a little, noncancerous (benign) mass of cells named polyps that structure within the colon. After a period of time these polyps can turn into advanced malignant tumors that attack the human body and some of these polyps can become colon cancers. So far, no concrete causes have been identified and the complete cancer treatment is very difficult to be detected by doctors in the medical field. Colon cancer often has no symptoms in an early stage so detecting it at this stage is curable but colorectal cancer diagnosis in the final stages (stage IV), gives it the opportunity to spread into different pieces of the body, which are difficult to treat successfully, and the person\u27s opportunities of survival become much lower. False diagnosis of colorectal cancer which means wrong treatment for patients with long-term infections and they will be suffering from colon cancer this causing the death for these patients. Also, cancer treatment needs more time and a lot of money. This paper provides a comparative study for methodologies and algorithms used in the colon cancer diagnoses and detection this can help for proposing a prediction for risk levels of colon cancer disease using CNN algorithm of deep learning (Convolutional Neural Networks Algorithm)

Object Detection in medical imaging

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsArtificial Intelligence, assisted by deep learning, has emerged in various fields of our society. These systems allow the automation and the improvement of several tasks, even surpassing, in some cases, human capability. Object detection methods are used nowadays in several areas, including medical imaging analysis. However, these methods are susceptible to errors, and there is a lack of a universally accepted method that can be applied across all types of applications with the needed precision in the medical field. Additionally, the application of object detectors in medical imaging analysis has yet to be thoroughly analyzed to achieve a richer understanding of the state of the art. To tackle these shortcomings, we present three studies with distinct goals. First, a quantitative and qualitative analysis of academic research was conducted to gather a perception of which object detectors are employed, the modality of medical imaging used, and the particular body parts under investigation. Secondly, we propose an optimized version of a widely used algorithm to overcome limitations commonly addressed in medical imaging by fine-tuning several hyperparameters. Thirdly, we develop a novel stacking approach to augment the precision of detections on medical imaging analysis. The findings show that despite the late arrival of object detection in medical imaging analysis, the number of publications has increased in recent years, demonstrating the significant potential for growth. Additionally, we establish that it is possible to address some constraints on the data through an exhaustive optimization of the algorithm. Finally, our last study highlights that there is still room for improvement in these advanced techniques, using, as an example, stacking approaches. The contributions of this dissertation are several, as it puts forward a deeper overview of the state-of-the-art applications of object detection algorithms in the medical field and presents strategies for addressing typical constraints in this area.A Inteligência Artificial, auxiliada pelo deep learning, tem emergido em diversas áreas da nossa sociedade. Estes sistemas permitem a automatização e a melhoria de diversas tarefas, superando mesmo, em alguns casos, a capacidade humana. Os métodos de detecção de objetos são utilizados atualmente em diversas áreas, inclusive na análise de imagens médicas. No entanto, esses métodos são suscetíveis a erros e falta um método universalmente aceite que possa ser aplicado em todos os tipos de aplicações com a precisão necessária na área médica. Além disso, a aplicação de detectores de objetos na análise de imagens médicas ainda precisa ser analisada minuciosamente para alcançar uma compreensão mais rica do estado da arte. Para enfrentar essas limitações, apresentamos três estudos com objetivos distintos. Inicialmente, uma análise quantitativa e qualitativa da pesquisa acadêmica foi realizada para obter uma percepção de quais detectores de objetos são empregues, a modalidade de imagem médica usada e as partes específicas do corpo sob investigação. Num segundo estudo, propomos uma versão otimizada de um algoritmo amplamente utilizado para superar limitações comumente abordadas em imagens médicas por meio do ajuste fino de vários hiperparâmetros. Em terceiro lugar, desenvolvemos uma nova abordagem de stacking para aumentar a precisão das detecções na análise de imagens médicas. Os resultados demostram que, apesar da chegada tardia da detecção de objetos na análise de imagens médicas, o número de publicações aumentou nos últimos anos, evidenciando o significativo potencial de crescimento. Adicionalmente, estabelecemos que é possível resolver algumas restrições nos dados por meio de uma otimização exaustiva do algoritmo. Finalmente, o nosso último estudo destaca que ainda há espaço para melhorias nessas técnicas avançadas, usando, como exemplo, abordagens de stacking. As contribuições desta dissertação são várias, apresentando uma visão geral em maior detalhe das aplicações de ponta dos algoritmos de detecção de objetos na área médica e apresenta estratégias para lidar com restrições típicas nesta área

Sessile serrated lesions in focus: Examining temporal trends, patient risk factors, and the role of the endoscopist in lesion detection

Serrated polyps of the colorectum have become increasingly recognized as an important clinical entity, as these precursor lesions are hypothesized to be responsible for up to 25% of sporadic cases of colorectal cancer. Much confusion exists regarding these polyps; particularly, their classification and associated malignant risk due to varied nomenclature, evolving pathological criteria, and ongoing research in prognostication. A specific subtype, sessile serrated lesions (SSLs), are of particular interest, as they are the most prevalent premalignant subtype and are over-represented in cases of interval cancers. Accurate identification and risk assessment remains a challenge owing to variable detection of clinically relevant serrated lesions by endoscopists, high inter-observer variability in diagnosis by pathologists, and an incomplete understanding of risk of future neoplasia. In this thesis, we analyze over 75,000 screening colonoscopies performed over a five-year period at a dedicated, large volume, high-efficiency screening centre to identify trends in the endoscopic detection of SSLs. The intent of this work is to better understand the temporal factors influencing SSL detection prevalence, the patient risk factors that are associated with these lesions, and how detection is related to procedural and endoscopist factors. The analysis includes consideration of traditional statistical methods as well as novel machine learning algorithms. We demonstrated a positive temporal trend in SSL detection over study period and identified several patient, procedural, and endoscopist factors associated with SSL detection. Machine learning models improved upon the predictive capabilities of traditional statistical models, yet a significant proportion of variability in risk remained unexplained, underscoring the complexity of accurately predicting SSLs. Endoscopic detection of SSLs demonstrates strong correlation with other detection metrics, notably adenoma detection rate, implying a shared underlying skillset requisite for the identification of these distinct polyp types. This connection highlights opportunities for enhancing detection through benchmarking and established quality improvement strategies

Segmentation and polyp detection in virtual colonoscopy : a complete system for computer aided diagnosis

El cancer colorectal es una de las mayores causas de muerte por cancer en el mundo. La deteccion temprana de polipos es fundamental para su tratamiento, permitiendo alcanzar tasas del 90% de curabilidad. La tecnica habitual para la deteccion de polipos, debido a su elevada performance, es la colonoscopia optica (tecnica invasiva y extremadamente cara). A mediados de los '90 surge la tecnica denominada colonoscopia virtual. Esta tecnica consiste en la reconstruccion 3D del colon a partir de cortes de tomografia computada. Es por ende una tecnica no invasiva, y relativamente barata, pero la cantidad de falsos positivos y falsos negativos producida por estos metodos esta muy por encima de los maximos aceptados en la practica medica. Los avances recientes en las tecnicas de imagenologia parecerian hacer posible la reduccion de estas tasas. Como consecuencia de esto, estamos asistiendo a un nuevo interes por la colonoscopia virtual. En este trabajo se presenta un sistema completo de diagnostico asistido por computadora. La primera etapa del sistema es la segmentacion, que consiste en la reconstruccion 3D de la superficie del colon a partir del volumen tomografico. El aporte principal en este paso es el suavizado de la imagen. A partir de la superficie, se detectan aquellas zonas candidatas de ser polipos mediante una estrategia multi-escala que permite delinear con precision la zona. Luego para cada candidato se extraen caracteristicas geometricas y de textura, que son calculadas tambien en el tejido que rodea la zona a efectos de compararlas. Finalmente las zonas candidatas se clasifican utilizando SVM. Los resultados obtenidos son prometedores, permitiendo detectar un 100% de los polipos mayoresColorectal cancer is the second leading cause of cancer-related death in the United States, and the third cause worldwide. The early detection of polyps is fundamental, allowing to reduce mortality rates up to 90%. Nowadays, optical colonoscopy is the most used detection method due in part to its relative high performance. Virtual Colonoscopy is a promising alternative technique that emerged in the 90's. It uses volumetric Computed Tomographic data of the cleansed and air-distended colon, and the examination is made by a specialist from the images in a computer. Therefore, this technique is less invasive and less expensive than optical colonoscopy, but up to now the false positive and false negative rates are above the accepted medical limits. Recent advances in imaging techniques have the potential to reduce these rates; consequently, we are currently re-experiencing an increasing interest in Virtual Colonoscopy. In this work we propose a complete pipeline for a Computer-Aided Detection algorithm. The system starts with a novel and simple segmentation step. We then introduce geometrical and textural features that take into account not only the candidate polyp region, but the surrounding area at multiple scales as well. This way, our proposed CAD algorithm is able to accurately detect candidate polyps by measuring local variations of these features. Candidate patches are then classi ed using SVM. The whole algorithm is completely automatic and produces state-of-the-art results, achieving 100% sensitivity for polyps greater than 6mm in size with less than one false positive per case, and 100% sensitivity for polyps greater than 3mm in size with 2:2 false positives per case

Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems

Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer. Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership

Classification of Anomalies in Gastrointestinal Tract Using Deep Learning

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have been proposed by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems

Colon histology slide classification with deep-learning framework using individual and fused features

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively