An optimal self adaptive deep neural network and spine‐kernelled chirplet transform for image registration

Abstract

Image registration is one of the image processing techniques that align more than two images of a similar scene captured under different perspectives at different intervals of time. In demographical research, the image registration process assists to study differences in the structure of brain tissue. Due to enhanced technological advancements, numerous image registration methods have been established. On the other hand, these traditional techniques face few real-time challenges while processing huge input data. In addition to this, uncertainty analysis becomes a crucial step in medical applications which is utilized to judge whether the registration result is valuable or not.The high percentage of uncertainty than the threshold makes the registration result abnormal. Therefore, to conquer such circumstances, this research work proposed a modified spine-kernelled chirplet transform (MCST) based optimal Self-Adaptive Deep Neural Network (SADNN) which focuses mainly on enhancing registration accuracy by reducing the uncertainties of registration results. The experimental analysis is conducted and from the evaluation results, the proposed MCST-based optimal SADNN technique outperforms existing techniques in terms of accuracy, specificity, sensitivity, F-measure, and DICE values. Moreover, the proposed method achieves 97.2% accuracy for accurate image registration.

An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model

“…10 Various classes of computer-aided diagnosis systems are utilized to detect cancer, for the past few years. [11][12][13][14] The conventional computer vision algorithm is utilized as the classifier for extracting a larger number of characteristics like texture, size, color, and shape for detecting cancer. 15 Early detection and accurate diagnosis of skin cancer will assist the proper medical treatment and healing process as well as prevents the worse effects of the skin cancer.…”