Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images

Abstract

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.

A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images

“…As the hemorrhage is only segmented from the images, it can be considered instance segmentation. This study used a U-Net (encoder-decoder)-like convolutional neural network (CNN) to instance segment the area of hemorrhage presented in a CT image since it performed better than other deep learning networks [21].Several pre-trained models trained on the ImageNet dataset served as encoders in segmentation tasks [22]. A pre-processing step was first performed on the raw data before different variants of the U-Net model were trained to predict ICH masks.…”

A Metabolomics-Based Study on the Discriminative Classification Models and Toxicological Mechanism of Estazolam Fatal Intoxication

“…Phenylacetylglycine, creatine and indole-3-lactic acid were significantly up-regulated in the EFI, compared with the four NDRDs groups (Figure 3A(b-d)). The specific procedure used 80% of the above 50 samples as the training set [39] (n EFI = 8, n NDRDs = 32). The results showed that the classification model consisting of these three candidate differentials based on 100 cross-validations had an AUC value equal to 1 (Figure 3B(a)).…”

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

“…Also, a combination of SVD [8] and PCA [41] as a feature reduction technique with data balancing technologies such as SMOTE and ADASYN is used. Filtering and thresholding, object detection, erosion and dilation, boundary detection, and lane extraction [42] are used for image datasets. DSIFT and DTL2 [43] are used for features of images.…”

“…Automated evaluation of thalassemia has been studied using a unique deep-learningbased method [42] for thalassemia screening. The main goal of the project is to automatically obtain the tracks from electrophoresis envision strips and classify individuals as normal or abnormal with thalassemia.…”