Feature Selection Using Random Forest Algorithm to Diagnose Tuberculosis From Lung CT Images

Tuberculosis is one of the hazardous infectious diseases that can be categorized by the evolution of tubercles in the tissues. This disease mainly affects the lungs and also the other parts of the body. The disease can be easily diagnosed by the radiologists. The main objective of this chapter is to get best solution selected by means of modified particle swarm optimization is regarded as optimal feature descriptor. Five stages are being used to detect tuberculosis disease. They are pre-processing an image, segmenting the lungs and extracting the feature, feature selection and classification. These stages that are used in medical image processing to identify the tuberculosis. In the feature extraction, the GLCM approach is used to extract the features and from the extracted feature sets the optimal features are selected by random forest. Finally, support vector machine classifier method is used for image classification. The experimentation is done, and intermediate results are obtained. The proposed system accuracy results are better than the existing method in classification.

A Survey on Early Detection of Women's Breast Cancer Using IoT

The internet of things is probably one of the most challenging and disruptive concepts raised in recent years. Recent development in innovation and availability have prompted the rise of internet of things (IoT). IoT technology is used in a wide scope of certified application circumstances. Internet of things has witnessed the transition in life for the last few years which provides a way to analyze both the real-time data and past data by the emerging role. The current state-of-the-art method does not effectively diagnose breast cancer in the early stages. Thus, the early detection of breast cancer poses a great challenge for medical experts and researchers. This chapter alleviates this by developing a novel software to detect breast cancer at a much earlier stage than traditional methods or self-examination.

Segmentation of Spine Tumour Using K-Means and Active Contour and Feature Extraction Using GLCM

MRI imaging technique is used to detect spine tumours. After getting the spine image through MRI scans calculation of area, size, and position of the spine tumour are important to give treatment for the patient. The earlier the tumour portion of the spine is detected using manual labeling. This is a challenging task for the radiologist, and also it is a time-consuming process. Manual labeling of the tumour is a tiring, tedious process for the radiologist. Accurate detection of tumour is important for the doctor because by knowing the position and the stage of the tumour, the doctor can decide the type of treatment for the patient. Next, important consideration in the detection of a tumour is earlier diagnosis of a tumour; this will improve the lifetime of the patient. Hence, a method which helps to segment the tumour region automatically is proposed. Most of the research work uses clustering techniques for segmentation. The research work used k-means clustering and active contour segmentation to find the tumour portion.

Correlation and Analysis of Overlapping Leukocytes in Blood Cell Images Using Intracellular Markers and Colocalization Operation

This chapter contributes to the study of uncertainty of signal dimensions within a microscopic image of blood sample. Appropriate colocalization indicator classifies the leukocytes in the region of interest having ragged boundaries. Signal transduction has been interpreted using correlation function determined fluorescence intensity in proposed work using just another colocalization plugin (JaCoP). Dependence between the channels in the colocalization region is being analysed in a linear fashion using Pearson correlation coefficient. Manders split, which gives intensity, is represented in a channel by co-localizing pixels. Overlap coefficients are also being analysed to analyse coefficient of each channel. Li's intensity correlation coefficient is being used in specific cases to interpret the impact of staining.

An Ensemble Feature Subset Selection for Women Breast Cancer Classification

Breast cancer leads to fatal diseases both in India and America and takes the lives of thousands of women in the world every year. The patients can be easily treated if the signs and symptoms are identified at the early stages. But the symptoms identified at the final stage spreads in the human body, and most of the time, the cancer is identified at the final stage. Breast cancer detected at the early stage is treated easily rather than at the advanced stage. Computer-aided diagnosis came into existence from 2000 with high expectations to improve true positive diagnosis and reduce false positive marks. Artificial intelligence revolved in computing drives the attention of deep learning for an automated breast cancer detection and diagnosis in digital mammography. The chapter focuses on automatic feature selection algorithm for diagnosis of women breast cancer from digital mammographic images achieved through multi-layer perceptron techniques.

Enchodroma Tumor Detection From MRI Images Using SVM Classifier

Magnetic resonance imaging is a standard modality used in medicine for bone diagnosis and treatment. It offers the advantage to be a non-invasive technique that enables the analysis of bone tissues. The early detection of tumor in the bone leads on saving the patients' life through proper care. The accurate detection of tumor in the MRI scans are very easy to perform. Furthermore, the tumor detection in an image is useful not only for medical experts, but also for other purposes like segmentation and 3D reconstruction. The manual delineation and visual inspection will be limited to avoid time consumption by medical doctors. The bone tumor tissue detection allows localizing a mass of abnormal cells in a slice of magnetic resonance (MR).

Detection of Tumor From Brain MRI Images Using Supervised and Unsupervised Methods

Brain tumor discovery and its segmentation from the magnetic resonance images (MRI) is a difficult task that has convoluted structures that make it hard to section the tumor with MR cerebrum images, different tissues, white issue, gray issue, and cerebrospinal liquid. A mechanized grouping for brain tumor location and division helps the patients for legitimate treatment. Additionally, the method improves the analysis and decreases the indicative time. In the separation of cerebrum tumor, MRI images would focus on the size, shape, area, and surface of MRI images. In this chapter, the authors have focused various supervised and unsupervised clustering techniques for identifying brain tumor and separating it using convolutional neural network (CNN), k-means clustering, fuzzy c-means grouping, and so on.

Machine Learning in Healthcare

Machine learning is a popular approach in the field of healthcare. Healthcare is an important industry that provides service to millions of people and as well as at the same time becoming top revenue earners in many countries. Machine learning in healthcare helps to analyze thousands of different data points and suggest outcomes, provide timely risk factors, optimize resource allocation. Machine learning is playing a critical role in patient care, billing processing to set the target to marketing and sales team, and medical records for patient monitoring and readmission, etc. Machine learning is allowing healthcare specialists to develop alternate staffing models, intellectual property management, and using the most effective way to capitalize on developed intellectual property assets. Machine learning approaches provide smart healthcare and reduce administrative and supply costs. Today healthcare industry is committed to deliver quality, value, and satisfactory outcomes.

Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder

In this chapter, the authors used autoencoder in data preprocessing step in an attempt to improve image representation, consequently increasing classification performance. The authors applied autoencoder to the task of breast lesion classification in mammographic images. Image Retrieval in Medical Applications (IRMA) database was used. This database has a total of 2,796 ROI (regions of interest) images from mammograms. The images are from patients in one of the three conditions: with a benign lesion, a malignant lesion, or presenting healthy breast. In this study, images were from mostly fatty breasts and authors assessed different intelligent algorithms performance in grouping the images in their respective diagnosis.

An Approach to Cloud Computing for Medical Image Analysis

Cloud computing has become popular among users in organizations and companies. Security and efficiency are the two major problems facing cloud service providers and their customers. Cloud data allocation facilities that allow groups of users to work together to access the shared data are the most standard and effective working styles in the enterprises. So, in spite of having advantages of scalability and flexibility, cloud storage service comes with confidential and security concerns. A direct method to defend the user data is to encrypt the data stored at the cloud. In this research work, a secure cloud model (SCM) that contains user authentication and data scheduling approach is scheduled. An innovative digital signature with chaotic secure hashing (DS-CS) is used for user authentication, followed by an enhanced work scheduling based on improved genetic algorithm to reduce the execution cost.