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

Fragile Watermarking for Image Authentication Using QR factorization and Fourier Transform

Image authentication technique is one of the important methods for a large number of multimedia applications. When a digital image is passed over non-secure channels such as the Internet, it may be changed and manipulated. For some important images such as military and medical images, these manipulations are very harmful and such images should be protected against them. There are several ways such as fragile and semi-fragile watermarking to authenticate images from malicious attacks. This paper presents a fragile watermarking algorithm for image authentication by using QR factorization and Fourier Transform (FT). By applying Fourier transform to host image, frequency domain which causes visual quality in watermarking is achieved. After applying FT, it is factorized by QR decomposition. QR factorization is also applied to watermark image. After factorizing both images, a coefficient of the upper triangular matrix R from watermark image is embedded to the upper triangular matrix R from host image. So a sign of the watermark image is hidden in the host image. This method is a fragile watermarking and it is sensitive to a little attack. So if an image is attacked over the Internet, the watermark image can not be extracted and it means that it has been attacked and it helps us to recognize if an image is changed after being transmitted over the Internet. The experimental results show that this method is sensitive to every weak attack and extraction part can not extract watermark image if it has been attacked