A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

A Review on Classification of White Blood Cells Using Machine Learning Models

The machine learning (ML) and deep learning (DL) models contribute to exceptional medical image analysis improvement. The models enhance the prediction and improve the accuracy by prediction and classification. It helps the hematologist to diagnose the blood cancer and brain tumor based on calculations and facts. This review focuses on an in-depth analysis of modern techniques applied in the domain of medical image analysis of white blood cell classification. For this review, the methodologies are discussed that have used blood smear images, magnetic resonance imaging (MRI), X-rays, and similar medical imaging domains. The main impact of this review is to present a detailed analysis of machine learning techniques applied for the classification of white blood cells (WBCs). This analysis provides valuable insight, such as the most widely used techniques and best-performing white blood cell classification methods. It was found that in recent decades researchers have been using ML and DL for white blood cell classification, but there are still some challenges. 1) Availability of the dataset is the main challenge, and it could be resolved using data augmentation techniques. 2) Medical training of researchers is recommended to help them understand the structure of white blood cells and select appropriate classification models. 3) Advanced DL networks such as Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN can also be used in future techniques.Comment: 23 page

Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry

Deep learning has achieved spectacular performance in image and speech recognition and synthesis. It outperforms other machine learning algorithms in problems where large amounts of data are available. In the area of measurement technology, instruments based on the photonic time stretch have established record real-time measurement throughput in spectroscopy, optical coherence tomography, and imaging flow cytometry. These extreme-throughput instruments generate approximately 1 Tbit/s of continuous measurement data and have led to the discovery of rare phenomena in nonlinear and complex systems as well as new types of biomedical instruments. Owing to the abundance of data they generate, time-stretch instruments are a natural fit to deep learning classification. Previously we had shown that high-throughput label-free cell classification with high accuracy can be achieved through a combination of time-stretch microscopy, image processing and feature extraction, followed by deep learning for finding cancer cells in the blood. Such a technology holds promise for early detection of primary cancer or metastasis. Here we describe a new deep learning pipeline, which entirely avoids the slow and computationally costly signal processing and feature extraction steps by a convolutional neural network that directly operates on the measured signals. The improvement in computational efficiency enables low-latency inference and makes this pipeline suitable for cell sorting via deep learning. Our neural network takes less than a few milliseconds to classify the cells, fast enough to provide a decision to a cell sorter for real-time separation of individual target cells. We demonstrate the applicability of our new method in the classification of OT-II white blood cells and SW-480 epithelial cancer cells with more than 95% accuracy in a label-free fashion

Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non- leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction

A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells

Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person's blood sample under a microscope. They identify and categorize leukemia by counting various blood cells and morphological features. This technique is time-consuming for the prediction of leukemia. The pathologist's professional skills and experiences may be affecting this procedure, too. In computer vision, traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images such as microscopic blood cells. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells. First, we have divided the previous works into six categories based on the output of the models. Then, we describe various steps of detection and classification of acute leukemia and WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction), Classification, and focus on classification step in the methods. Finally, we divide automated detection and classification of acute leukemia and WBCs into three categories, including traditional, Deep Neural Network (DNN), and mixture (traditional and DNN) methods based on the type of classifier in the classification step and analyze them. The results of this study show that in the diagnosis and classification of acute leukemia and WBCs, the Support Vector Machine (SVM) classifier in traditional machine learning models and Convolutional Neural Network (CNN) classifier in deep learning models have widely employed. The performance metrics of the models that use these classifiers compared to the others model are higher