Automatic Detection of Acute Lymphoblastic Leukemia Using UNET Based Segmentation and Statistical Analysis of Fused Deep Features

Acute lymphoblastic leukemia (ALL) in human white blood cells is hazardous and requires immediate clinical interventions. The main objective of the proposed work is to suggest the predominant features for detection of ALL. The input images are obtained from public database ‘ALL-IDB2ʹ. All the obtained images are resized into a uniform size. The nucleus of both healthy and blast cells is segmented using UNET. Thousand deep features are extracted from the fully connected layer of different convolutional neural network models such as AlexNet, GoogleNet and SqueezeNet, and all features are fused together. The distinct features are selected using mutual information (MI), minimum recursive maximal relevance (mRmR) and recursive feature elimination (RFE) based methods. Furthermore, the intersection of selected features is carried out to obtain the prominent deep features, which are examined by heatmap. Finally, the statistical analysis is carried out with consistent and robust feature sets using ANOVA. It is found that 50% of the fused deep features seem to be better with p = . 00001. The performance of the proposed system without feature fusion is also observed. It is detected that fused features are more suitable to discriminate the healthy and blast cells to identify ALL and support clinical decisions.