Trans-SegNet - Deep Transfer Learning Approach to Detect Abnormalities in Microscopic Blood Smear Images for Medical Image Segmentation

Medical image segmentation is very important for detecting illness and treatment in a precise manner. The diseases like leukemia, malaria, and other blood disorders identification mainly depends on the anomalies segmentation in microscopic blood smear images. Traditional methods for medical image segmentation are laborious to unpredictability because they need a frequent domain-specific expertise and manual intervention. The study provides a novel method to segment microscopic blood smears using Trans-SegNet, a deep transfer learning algorithm. By using pre-trained Convolutional Neural Networks (CNNs), the proposed method transfer learned features efficiently to the blood smear analysis domain, so that segmentation accuracy can be improved. A pre-trained CNN is fine-tuned using blood smear images properties and for accurate segmentation, the proposed method uses a Fully Convolutional Network (FCN). Experimentation uses publicly accessible blood smear image dataset and results show that the proposed method outperforms traditional segmentation techniques. The Trans-SegNet obtained an Intersection over Union (IoU) of 89.7% and Dice Similarity Coefficient (DSC) of 92.5%. Furthermore, precision with 91.3% and recall with 90.8% were recorded.