White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.

The deadly coronavirus virus (COVID‐19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID‐19 is the reverse‐transcription polymerase chain reaction (RT‐PCR), the process is affected by a scarcity of RT‐PCR kits as well as its complexities. Medical imaging using machine learning and deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but to train machine learning features needs to be extracted manually, and in deep learning, efficiency is affected by deep learning architecture and low data. In this study, handcrafted local binary pattern (LBP) and automatic seven deep learning models extracted features were used to train support vector machines (SVM) and K‐nearest neighbour (KNN) classifiers, to improve the performance of the classifier, a concatenated LBP and deep learning feature was proposed to train the KNN and SVM, based on the performance criteria, the models VGG‐19 + LBP achieved the highest accuracy of 99.4%. The SVM and KNN classifiers trained on the hybrid feature outperform the state of the art model. This shows that the proposed feature can improve the performance of the classifiers in detecting COVID‐19.

Skin lesions can be in malignant or benign forms. Benign skin lesion types are not deadly; however, malignant types of skin lesions can be fatal. Lethal forms are known as skin cancer. These types require urgent clinical treatment. Fast detection and diagnosis of malignant types of skin lesions might prevent life‐threatening scenarios. This work presents two methods for the automatic classification of malignant melanoma and seborrhoeic keratosis lesions. The first method builds on modelling skin images together with wavelet coefficients. Approximate, horizontal, and vertical wavelet coefficients are obtained using the wavelet transform, and then deep learning (DL) models are generated for each of the representations and skin images. The second method builds on modelling skin images together with three approximate coefficients. This method utilises a sequential wavelet transformation to produce approximation coefficients. Then DL models are generated for each of the representations and skin images. Transfer learning‐based ResNet‐18 and ResNet‐50 DL models provide model images and wavelet coefficients. Then skin lesion detection is achieved by fusing model output probabilities. Both proposed models outperform the methods only based on image data and other previously proposed methods.