ABSTRACT
The two most generally diagnosed Neurodegenerative diseases are the Alzheimer and Parkinson diseases. So this paper presents a fully automated early screening system based on the Capsule network for the classification of these two Neurodegenerative diseases. In this study, we hypothesized that the Neurodegenerative diseases-Caps system based on the Capsule network architecture accurately performs the multiclass i.e. three class classification into either the Alzheimer class or Parkinson class or Healthy control and delivers better results in comparison other deep transfer learning models. The real motivation behind choosing the capsule network architecture is its more resilient nature towards the affine transformations as well as rotational & translational invariance, which commonly persists in the medical image datasets. Apart from this, the capsule networks overcomes the pooling layers related deficiencies from which conventional CNNs are mostly affected and unable to delivers accurate results especially in the tasks related to image classification. The various Computer aided systems based on machine learning for the classification of brain tumors and other types of cancers are already available. Whereas for the classification of Neurodegenerative diseases, the amount of research done is very limited and the number of persons suffering from this type of diseases are increasing especially in developing countries like India, China etc. So there is a need to develop an early screening system for the correct multiclass classification into Alzheimer's, Parkinson's and Normal or Healthy control cases. The Alzheimer disease and Parkinson progression (ADPP) dataset is used in this research study for the training of the proposed Neurodegenerative diseases-Caps system. This ADPP dataset is developed with the aid of both the Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's disease Neuroimaging Initiative (ADNI) databases. There is no such early screening system exist yet, which can perform the accurate classification of these two Neurodegenerative diseases. For the sake of genuine comparison, other popular deep transfer learning models like VGG19, VGG16, ResNet50 and InceptionV3 are implemented and also trained over the same ADPP dataset. The proposed Neurodegenerative diseases-Caps system deliver accuracies of 97.81, 98, 96.81% for the Alzheimer, Parkinson and Healthy control or Normal cases with 70/30 (training/validation split) and performs way better as compare to the other popular Deep transfer learning models. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09787-1.
ABSTRACT
Temporomandibular joint ankyloses (TMJA) may manifest in patients with several predisposing systemic conditions. A case of extraarticular TMJA is presented in a patient diagnosed with fibrodysplasia ossificans progressive (FOP) is presented. The features, diagnosis, and management of TMJA superimposed on this condition are presented through a qualitative systematic review of literature.
