Early knee osteoarthritis classification using distributed explainable convolutional neural network with local interpretable model-agnostic explanations

Abstract Knee Osteoarthritis (KOA) is a type of Knee Arthritis (KA) that causes pain, swelling, and other discomforts to the knee joints, which is quite complicated to classify using previous methods due to its various limitations such as computational cost, over-fitting issues, less reliability and so on. In this research, the classification using a distributed explainable convolutional neural network with local interpretable model-agnostic explanations (LExNN) model is proposed for knee Osteoarthritis. The distributed LExNN model is an ensemble with a distributed mechanism, in which the input vectors are distributed to two explainable CNNs which makes it quite easier for classification and grading. The distributed concepts are blown up with several advantages such as high computation efficiency with less training time, reliability, and develop an efficient model for classification. In addition, the local interpretable model-agnostic explanations (LIME) technique interprets important information from the image and classifies the severity based on two grades namely high and low. This technique provides significant, simple, and understandable information, which is quite reliable for KOA classification. The supremacy of the model can be determined by measuring several parameters such as accuracy, precision, recall, and f1 score that gives 99.25%, 99.25%, 98.42%, and 98.83% compared to other state-of-the-art methods.