ACDSSNet: Atrous Convolution-Based Deep Semantic Segmentation Network for Efficient Detection of Sickle Cell Anemia

In medical image processing, semantic segmentation plays an important role since, in most applications, it is required to find the exact location of the anomaly. It is tough than the segmentation or classification task since in this task class-belongingness of each pixel is predicted. The presence of noise, and variations of viewpoint, shape, and size of cells make it more challenging. In this work, two novel Atrous Convolution-based Deep Semantic Segmentation Networks: ACDSSNet-I, ACDSSNet-II are proposed for more accurate Sickle Cell Anemia (SCA) detection, which can mitigate these issues. The main contributions are: 1) Improvement of feature extraction performance by employing Atrous convolution-based dense prediction, which yields varying field-view with adaptive resolution; 2) Employment of Atrous spatial pyramid-based pooling resulting in more robust segmentation; 3) Upgrading the segmentation performance by adding an efficient decoder module to finetune the segmentation, particularly at object boundaries; 4) Design of modified DeepLabV3+ architectures (MDA) by introducing computationally efficient MobileNetV2 or ResNet50 as a base classifier; 5) Further performance improvement has been accomplished by hybridizing MDA-1 with MDA-2 by integrating the benefits of MobileNetV2 models and ADAM and SGDM optimizers; 6) Improvement of overall performance by efficiently utilizing the input image's saturation information only to minimize the false positive. Furthermore, the optimal selection of threshold value makes the hybridization of MDA-1 with MDA-2 efficient resulting in more accurate semantic segmentation. The experimental results illustrate the proposed model outperforms others with the best semantic segmentation performances: 98.21% accuracy, 99.00% specificity, and 0.9547 DSC value.