Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images

Bone marrow (BM) plays a crucial role in the hematopoietic process, producing all of the body's blood cells and maintaining the overall immune and health system. Red and yellow BM are the two various kinds of BM. A comprehensive identification of these cells assists in the primary and precise recognition of these disorders. The recognition and identification of BM cells are crucial bases for haematology diagnostics. Physical study of BM detection and classification presently performed in medical laboratories can be primarily insufficient owing to various factors, such as prolonged and challenging. Recently, with the fast growth of deep learning (DL) and machine learning (ML) methods, object detection methods have been progressively used for cell detection. DL is a secondary domain of artificial intelligence (AI) methods able to spontaneously assess delicate graphical features to create exact predictions that have been newly popularized in various imaging-related tasks. This study proposes a Multimodal Transfer Learning with Snake Optimization on Bone Marrow Cell Classification (MTLSO-BMCC) technique using biomedical histopathological images. The main intention of the MTLSO-BMCC technique is to identify and classify BM cells utilizing HI. To achieve this, the presented MTLSO-BMCC method initially performs image preprocessing using a median filter (MF) for noise removal. Besides, the multimodal feature extraction process is accomplished in InceptionV3, Deep SqueezeNet, and SE-DenseNet models. The presented MTLSO-BMCC technique employs the hybrid kernel extreme learning machine (HKELM) method for the BM classification method. Finally, the snake optimization algorithm (SOA) is implemented to tune the parameter of the HKELM model. A widespread MTLSO-BMCC methodology simulation is accomplished under the BM Cell Classification dataset. The experimental validation of the MTLSO-BMCC methodology portrayed a superior accuracy value of 98.60% over existing approaches.