Leveraging Deep Learning Architecture optimization for enhanced early detection of bone marrow cancer.

Early bone marrow cancer detection improves patient outcomes and therapy success. This study introduces a deep learning method that enhances bone marrow cancer diagnosis by utilising various optimal designs. The framework preprocesses input data with a Gaussian filter to reduce noise and preserve critical features for exact analysis. GrabCut and Gaussian mixture models segment relevant data. This method accurately isolates sick regions, which is crucial for diagnosis. The feature extraction step uses a hybrid Gabor-GLCM filter method. Both texture-based and frequency-based aspects are captured to fully understand cell characteristics that distinguish healthy from cancerous tissue. The gathered data is then processed using a sequential convolutional neural network (CNN) tailored to the classification task. The framework performs well on a test dataset, with 0.999 accuracy, 0.999 precision, and 1.0 recall. These impressive measurements demonstrate the optimised deep learning approach's ability to diagnose bone marrow cancer early and accurately. Prompt action can improve therapy success and patient prognosis.