Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images

Haemoglobin (Hb) electrophoresis is a method of blood testing used to detect thalassaemia. However, the interpretation of the result of the electrophoresis test itself is a complex task. Expert haematologists, specifically in developing countries, are relatively few in number and are usually overburdened. To assist them with their workload, in this paper we present a novel method for the automated assessment of thalassaemia using Hb electrophoresis images. Moreover, in this study we compile a large Hb electrophoresis image dataset, consisting of 103 strips containing 524 electrophoresis images with a clear consensus on the quality of electrophoresis obtained from 824 subjects. The proposed methodology is split into two parts: (1) single-patient electrophoresis image segmentation by means of the lane extraction technique, and (2) binary classification (normal or abnormal) of the electrophoresis images using state-of-the-art deep convolutional neural networks (CNNs) and using the concept of transfer learning. Image processing techniques including filtering and morphological operations are applied for object detection and lane extraction to automatically separate the lanes and classify them using CNN models. Seven different CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, SqueezeNet and MobileNetV2) were investigated in this study. InceptionV3 outperformed the other CNNs in detecting thalassaemia using Hb electrophoresis images. The accuracy, precision, recall, f1-score, and specificity in the detection of thalassaemia obtained with the InceptionV3 model were 95.8%, 95.84%, 95.8%, 95.8% and 95.8%, respectively. MobileNetV2 demonstrated an accuracy, precision, recall, f1-score, and specificity of 95.72%, 95.73%, 95.72%, 95.7% and 95.72% respectively. Its performance was comparable with the best performing model, InceptionV3. Since it is a very shallow network, MobileNetV2 also provides the least latency in processing a single-patient image and it can be suitably used for mobile applications. The proposed approach, which has shown very high classification accuracy, will assist in the rapid and robust detection of thalassaemia using Hb electrophoresis images. 2022 by the authors.A part of the research was funded by the Higher Education Commission of Pakistan through its funded project of Artificial Intelligence in Healthcare, Intelligent Information Processing Lab, National Center of Artificial Intelligence.Scopu

Cyber secure consensus of fractional order multi-agent systems with distributed delays: Defense strategy against denial-of-service attacks

Leader-follower cyber secure consensus problem in cybersecurity is crucial as it directly addresses the need for secure and stable communication in a networked system, which is paramount in safeguarding against cyber threats like denial-of-service attacks. In this article, by utilizing control strategy with distributed delays in denial-of-service attack's presence for discrete-time multi-agent system of fractional-order, the leader-following cyber secure consensus problem is examined. Nonlinear functions have been assumed in the leader and follower equation of the system to study variations due to this function in the system. As multi-agent system is networked environment, in response to numerous threats, their security control becomes critically desirable, for example, denial of service. The consensus performance may be destabilized by resulting topologies which are caused by Denial-of-Service attacks. The connectivity between agents is destroyed, especially under connectivity-broken attacks. In order to overcome these problems, the strategy of novel defense which consists of consensus (with distributed delays) control is introduced. By using Caputo fractional difference operator sufficient criteria which includes the condition in terms LMI is derived and by using Lyapunov function approach, average dwell time and algebraic graph theory for security of addressed system's cyber secure consensus this is done for determining the obtained system of error's stability. Finally, by showing some numerical examples on the introduced systems, the effectiveness of the obtained results is determined

A novel metabarcoded 18S ribosomal DNA sequencing tool for the detection of Plasmodium species in malaria positive patients.

Various PCR based methods have been described for the diagnosis of malaria, but most depend on the use of Plasmodium species-specific probes and primers; hence only the tested species are identified and there is limited available data on the true circulating species diversity. Sensitive diagnostic tools and platforms for their use are needed to detect Plasmodium species in both clinical cases and asymptomatic infections that contribute to disease transmission. We have recently developed for the first time a novel high throughput ‘haemoprotobiome’ metabarcoded DNA sequencing method and applied it for the quantification of haemoprotozoan parasites (Theleria and Babesia) of livestock. Here, we describe a novel, high throughput method using an Illumina MiSeq platform to demonstrate the proportions of Plasmodium species in metabarcoded DNA samples derived from human malaria patients. Plasmodium falciparum and Plasmodium vivax positive control gDNA was used to prepare mock DNA pools of parasites to evaluate the detection threshold of the assay for each of the two species. The different mock pools demonstrate the accurate detection ability and to show the proportions of each of the species being present. We then applied the assay to malaria-positive human samples to show the species composition of Plasmodium communities in the Punjab province of Pakistan and in the Afghanistan-Pakistan tribal areas. The diagnostic performance of the deep amplicon sequencing method was compared to an immunochromatographic assay that is widely used in the region. The deep amplicon sequencing showed that P. vivax was present in 69.8%, P. falciparum in 29.5% and mixed infection in 0.7% patients examined. The immunochromatographic assay showed that P. vivax was present in 65.6%, P. falciparum in 27.4%, mixed infection 0.7% patients and 6.32% malaria-positive cases were negative in immunochromatographic assay, but positive in the deep amplicon sequencing. Overall, metabarcoded DNA sequencing demonstrates better diagnostic performance, greatly increasing the estimated prevalence of Plasmodium infection. The next-generation sequencing method using metabarcoded DNA has potential applications in the diagnosis, surveillance, treatment, and control of Plasmodium infections, as well as to study the parasite biology.•We reported for the first time the development of Haemoprotobiome technology to quantify the P. falciparum and P. vivax.•P. falciparum and P. vivax mock pools demonstrate the accurate detection ability and to show the proportions of each of the species.•Haemoprotobiome demonstrates better diagnostic performance than immunochromatographic assay