Hybrid deep layered network model based on multi-scale feature extraction and deep feature optimization for acute lymphoblastic leukemia anomaly detection.

Acute lymphoblastic leukemia (ALL), one of the common diseases of our day, is one of the most common hematological malignant diseases in childhood. Early diagnosis of ALL, which plays a critical role in medical diagnosis processes, is of great importance especially for the effective management of the treatment process of cancer patients. Therefore, ALL cells must be detected and classified correctly. Traditional methods used today prolong the detection and classification processes of cells, cause hematologists to interpret them according to their expertise, and delay medical decision-making processes. In this study, the performance of the hybrid model developed with different deep learning models for ALL diagnosis was comparatively analyzed. In the proposed ALL detection architecture, blood cell images were processed using the center-based cropping strategy and irrelevant areas in the images were automatically removed. The dataset was divided into training, validation, and test sets, and then features were extracted with deep hyperparameters for convolution, pooling, and activation layers using a model based on Xception architecture. The obtained features were optimized to the advanced Extreme Gradient Boosting (XGBoost) classifier and model classification results were obtained. The results showed that the proposed model achieved 98.88% accuracy. This high accuracy rate was compared with different hybrid models and it was seen that the model was more successful in detecting ALL disease compared to existing studies.