Leukemia is one of the most terminal types of blood cancer, and many people suffer from it every year. White blood cells (WBCs) have a significant association with leukemia diagnosis. Research studies reported that leukemia brings changes in WBC count and morphology. WBC accurate segmentation enables to detect morphology and WBC count which consequently helps in the diagnosis and prognosis of leukemia. Manual WBC assessment methods are tedious, subjective, and less accurate. To overcome these problems, we propose a multi-scale information fusion network (MIF-Net) for WBC segmentation. MIF-Net is a shallow architecture with internal and external spatial information fusion mechanisms. In WBC images, the cytoplasm is with low contrast compared to the background, whereas nuclei shape can be complex with an indistinctive boundary for some cases, therefore accurate segmentation becomes challenging. Spatial features in the initial layers of the network include fine boundary information and MIF-Net splits and propagates this boundary information on multi-scale for external information fusion. Multi-scale information fusion in our network helps in preserving boundary information and contributes to segmentation performance improvement. MIF-Net also uses internal information fusion after intervals for feature empowerment in different stages of the network. We evaluated our network for four publicly available datasets and achieved state-of-the-art segmentation performance. In addition, the proposed architecture exhibits superior computational efficiency by using only 2.67 million trainable parameters.

The rapidly increasing trend of retinal diseases needs serious attention, worldwide. Glaucoma is a critical ophthalmic disease that can cause permanent vision impairment. Typically, ophthalmologists diagnose glaucoma using manual assessments which is an error-prone, subjective, and time-consuming approach. Therefore, the development of automated methods is crucial to strengthen and assist the existing diagnostic methods. In fundus imaging, optic cup (OC) and optic disc (OD) segmentation are widely accepted by researchers for glaucoma screening assistance. Many research studies proposed artificial intelligence (AI) based decision support systems for glaucoma diagnosis. However, existing AI-based methods show serious limitations in terms of accuracy and efficiency. Variations in backgrounds, pixel intensity values, and object size make the segmentation challenging. Particularly, OC size is usually very small with unclear boundaries which makes its segmentation even more difficult. To effectively address these problems, a novel feature excitation-based dense segmentation network (FEDS-Net) is developed to provide accurate OD and OC segmentation. FEDS-Net employs feature excitation and information aggregation (IA) mechanisms for enhancing the OC and OD segmentation performance. FEDS-Net also uses rapid feature downsampling and efficient convolutional depth for diverse and efficient learning of the network, respectively. The proposed framework is comprehensively evaluated on three open databases: REFUGE, Drishti-GS, and Rim-One-r3. FEDS-Net achieved outperforming segmentation performance compared with state-of-the-art methods. A small number of required trainable parameters (2.73 million) also confirms the superior computational efficiency of our proposed method.