: Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes—chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL).
Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights
Lymphoma, a form of hematological disorder, arises due to uncontrolled proliferation of lymphocytes, a subset of leukocytes. The lymphocytes, which are found in the blood and lymphatic tissues of the human body, have a crucial role in protecting the individual from various diseases.
The lymphatic system comprises lymph nodes and lymphatic vessels responsible for draining fluid from bodily tissues and redirecting it to the circulatory system. Additionally, these structures aid in the removal of impaired, foreign, or aged cells. There are two types of lymphocytes, namely T and B. Both T and B l
. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis.
Keywords: malignant lymphoma; chronic lymphocytic leukemia (CLL); follicular lymphoma (FL); mantle cell lymphoma (MCL); transfer learning; DenseNet201; Inceptionv3; Xception; ensemble technique 1. Introduction Lymphoma, a form of hematological disorder, arises due to uncontrolled proliferation of lymphocytes, a subset of leukocytes.
The lymphocytes, which are found in the blood and lymphatic tissues of the human body, have a crucial role in protecting the individual from various diseases. The lymphatic system comprises
derived from the proposed system. Section 9 compares the performance of the proposed ensemble modelDiagnostics 2024 ,14, 469 3 of 25 with prior works. Lastly, Section 10 concludes the proposed system’s limitation and future work. 2.
Related Works This section provides an overview of several prior investigations that are pertinent to the identification of malignant lymphoma. All researchers intended to attain favorable outcomes via the implementation of distinct methodologies. 2.1. Machine Learning Methods for Malignant Lymphoma Classification Capobianco et al.
[ 1] proposed an ensemble model to find the Total Metabolic Tumor Volume (TMTV) calculated from F-labelled fluoro-2-deoxyglucose. Th
In this particular section, our primary focus is on the multiple source datasets em- ployed throughout the training and testing phases of five different CNN models such as VGG16, VGG19, DenseNet201, Inceptionv3, and Xception. An ensemble architecture is proposed to increase accuracy using InceptionV3 and Xception.
Training and testing are performed for the ensemble architecture using a multi-cancer lymphoma Kaggle dataset. Subsequently, we discuss the outcomes of the proposed ensemble learning model on theDiagnostics 2024 ,14, 469 12 of 25 mentioned CLL, FL, and MCL datasets. The pre-trained models are trained and tested at a learning rate of 0.001.
The proposed model has used the kaggle no
s and Scope for Future Research The diagnosis of malignant lymphoma cells faces numerous challenges in distinguish- ing different classes, particularly during the early stages. Artificial intelligence supports physicians in distinguishing the classes of malignant lymphoma.
In our work, the malig- nant lymphoma multi-class image datasets from various sources are trained using five pre-trained methodologies for diagnosing malignant lymphoma. The non-ensemble Convo- lutional Neural Network model is