The models evaluated resulted to AlexNet being the best pre-trained network to be working on the RCNN with an accuracy of 95.65%. The data used in training the models comprised of annotated image data represented as a ground truth table with image files and their corresponding bounding boxes coordinates. The system conducted a comparative analysis to determine which among the three pre-trained networks, AlexNet, GoogLeNet, and ResNet-18 will be the most suitable network to be integrated with Regions with Convolutional Neural Networks (RCNN) in performing multiple object detection in a given image. The diseases are namely: Black Rot, Black Measles, and Isariopsis. This study proposes a way for detecting three different diseases from grape leaves apart from the healthy leaves and considers the confidence value of the system in correctly identifying the classes. As the global population continues to arise and agricultural space continues to decline, every possible way of increasing the supply of food in any given condition and limited resources will address the above-mentioned problems. Identifying variant diseases in leaves is a significant method for optimizing food production. Based on WBC related literature study and its extensive analysis presented in this study, we derive future research directions for scientists and practitioners working in the MIA domain. They provide valuable information to medical specialists and help diagnose various hematic diseases such as AIDS and blood cancer (Leukaemia). The related literature study reveals that mainstream TML methods are vastly applied to microscopic blood smear images for white blood cells (WBC) analysis. The advanced DL techniques, particularly the evolving convolutional neural networks-based models in the MIA domain, are deeply investigated in this review article. The proposed review’s main impact is to find the most suitable TML and DL techniques in MIA, especially for leukocyte classification in blood smear images.
This review provides an in-depth analysis of available TML and DL techniques for MIA with a significant focus on leukocytes classification in blood smear images and other medical imaging domains, i.e., magnetic resonance imaging (MRI), CT images, X-ray, and ultrasounds. These methods substantially improved the diagnoses of automatic brain tumor and leukemia/blood cancer detection and can assist the hematologist and doctors by providing a second opinion. In computer vision, traditional machine learning (TML) and deep learning (DL) methods have significantly contributed to the advancements of medical image analysis (MIA) by enhancing prediction accuracy, leading to appropriate planning and diagnosis.
These techniques include traditional machine learning (TML), deep learning (DL), convolutional neural network (CNN) models, hybrid learning, and attention learning-based techniques to analyze medical image modalities to detect and classify cells in smear images. This study gives an extensive review of MIA but the research focuses more on the ML-based leucocytes/WBCs analysis in smear images. In the proposed review study, the recent and most relevant research papers are collected from IEEE, Science Direct, springer, and web of science (WoS) with the following keywords: ‘leucocytes detection’ or ‘leucocytes classification’. The main aim of this chapter is to identify high performer and suitable ML algorithms for WBCs analysis using blood microscopic smear images.
In this chapter, we provide a concise analysis of available ML techniques, to use these techniques for leucocytes analysis in microscopic images.
#ANACONDA PROMPT COMMANDS MANUAL#
Manual Detection, counting and classification of WBCs is very slow, challenging and boring task due to having complex overlapping and morphological uneven structure. An automatic leucocytes analysis provide valuable diagnostics facts to doctors, via they can automatically detect, blood cancer, brain tumor and significantly improve the hematological, pathological activities. Leucocytes analysis is the process of detection, localization, counting, analyzing WBCs, and it perform an active role in clinical hematology to assist health specialists in early stage disease diagnosing process. Different chronic hermitic diseases like blood cancer/leukemia, AIDs, malaria, anemia and even COVID-19, all these are diagnoses via analyzing leucocytes or white blood cells (WBCs). In recent development of machine learning (ML)-based medical image analysis that have contributed to the prediction, planning, and early diagnostic process.
0 kommentar(er)
