Classification of Malignant Lymphoma Using Interpretable Dilated <scp>MobileNetV2</scp> With Convolutional Recurrent Neural Network

ABSTRACT Lymphoma is commonly considered as cancer that affects the entire organs of the body. The accurate classification of malignant lymphomas is helpful for providing better treatment plans to patients. Usually, the lymphoma types are differentiated by cytologic features and growth patterns. Moreover, the abnormal cell variations are effectively identified through the immunologic, genetic, and clinical features that are useful in making the diagnosis. The gap between pattern analysis and cancer diagnostics is effectively handled with the development of computer vision methods. Computed tomography (CT)‐based image analysis and Positron Emission Tomography (PET)‐based image analysis for the classification of malignant lymphomas have some disadvantages, including a lack of inter and intra‐observer variability. Thus, a robust deep learning‐aided malignant lymphoma classification model is implemented to identify the specific type of lymphoma. Initially, the input images are acquired from benchmark databases. The required images are processed via the proposed Hybrid Adaptive and Attentive Networks (HAAN) for the malignant lymphoma classification. Therefore, it is the combination of Dilated MobilenetV2 with Convolutional‐Recurrent Neural Network (RNN) for the specific cancer subtype classification process. The functionality of the suggested network is enhanced by tuning the parameters in the network via the Revised Iteration‐based Peregrine Falcon Optimization (RIPFO). Thus, the proposed model processes large volumes of input images quickly and accurately for obtaining efficient malignant lymphoma classification results. Finally, the performance of the developed framework is estimated with conventional methods. The analysis results prove that the accuracy of the malignant lymphoma subtype classification framework is higher than the baseline classification mechanisms. In order to prove the model effectiveness, the accuracy of the developed technique shows 93.67%, 94.74%, and 95.36% in terms of diverse activation functions like linear, sigmoid, and ReLU, respectively.