Advancing Lymphoma Diagnosis in Histopathology Image Classification Using Multi Deep Learning Models

Deep learning's rapid development is generating significant interest in its potential to improve medical imaging. It has shown promising results in detecting malignant lymphoma in histopathology medical images. Image classification methods are widely used to aid in making diagnoses from medical images. In recent years, deep learning methods have achieved high performance in detecting malignant lymphoma in histopathology images. This study proposes a novel approach to improving lymphoma diagnosis in histopathology images called the Lightweight Convolutional Neural Network (LWCNN). The proposed LWCNN model comprises multiple deep learning architectures, including a convolutional neural network (CNN) that has been trained to classify lymphoma subtypes based on histopathology images using ResNet50 and MobileNetV2. The LWCNN model aggregates the predictions of disparate architectures to arrive at a definitive diagnosis, leveraging the unique capabilities of each. A comprehensive dataset of annotated lymphoma histopathology images was assembled for the purpose of training the multi-deep learning model. To ensure a representative and diverse training set, the dataset was meticulously curated to encompass various subtypes of lymphoma. Performance evaluation of the proposed deep learning model for lymphoma classification using standard metrics revealed the following accuracies: LWCNN (97.34% training, 86.71% testing), ResNet50 (88.76% training, 86.37% testing), and MobileNetV2 (88.47% training, 86.60% testing). These results indicate that the LWCNN model significantly surpasses existing approaches in diagnostic accuracy.