Radiomic features based classifiers and neural networks have shown promising results in tumor classification. The classification performance can be further improved greatly by exploring and incorporating the discriminative features towards cancer into mathematical models. In this research work, we have developed two radiomics driven likelihood models in Computed Tomography(CT) images to classify lung, colon, head and neck cancer. Initially, two diagnostic radiomic signatures were derived by extracting 105 3-D features from 200 lung nodules and by selecting the features with higher average scores from several supervised as well as unsupervised feature ranking algorithms. The signatures obtained from both the ranking approaches were integrated into two mathematical likelihood functions for tumor classification. Validation of the likelihood functions was performed on 265 public data sets of lung, colon, head and neck cancer with high classification rate. The achieved results show robustness of the models and suggest that diagnostic mathematical functions using general tumor phenotype can be successfully developed for cancer diagnosis.

Objective: To develop a Bayesian inversion framework on longitudinal chest CT scans which can perform efficient multi-class classification of lung cancer. Methods: While the unavailability of large number of training medical images impedes the performance of lung cancer classifiers, the purpose built deep networks have not performed well in multi-class classification. The presented framework employs particle filtering approach to address the non-linear behaviour of radiomic features towards benign and cancerous (stages I, II, III, IV) nodules and performs efficient multi-class classification (benign, early stage cancer, advanced stage cancer) in terms of posterior probability function. A joint likelihood function incorporating diagnostic radiomic features is formulated which can compute likelihood of cancer and its pathological stage. The proposed research study also investigates and validates diagnostic features to discriminate accurately between early stage (I, II) and advanced stage (III, IV) cancer. Results: The proposed stochastic framework achieved 86% accuracy on the benchmark database which is better than the other prominent cancer detection methods. Conclusion: The proposed classification framework can aid radiologists in accurate interpretation of lung CT images at an early stage and can lead to timely medical treatment of cancer patients.

The new emerging networks such as smart grids, smart homes and Internet of Things have enabled user accessibility across the globe and employ non-orthogonal multiple access (NOMA) scheme to accommodate huge number of connected devices. These devices which include smart meters, sensor and actuators etc. suffer from impulse noise (IN) while operating with power systems. Furthermore, NOMA scheme provides power domain multiple access (PDMA) which is found to be susceptible to IN. Based on the aforementioned IN intervention and its degrading effect on communication applications, novel mechanisms are desired to mitigate and classify the IN induced in the received signal. In this research work, novel IN mitigation and classification techniques are presented using deep learning methods for NOMA-based communication systems. The IN detection is performed by first identifying the IN occurrences using a deep neural network (DNN) which learns statistical traits of noisy samples followed by removal of harmful effect of IN in the detected occurrences. Using the proposed DNN, higher bit error rates (BER) were achieved when compared with the existing IN detection methods. The proposed method was further validated for high and low IN, and weak and strong IN occurrence probabilities. Moreover, another deep learning network is proposed in this research work to effectively distinguish between high IN and low IN in the noise contaminated NOMA symbols which can help improve the performance of IN detection models. Both of the deep learning methods proposed in this study show strong potential to address IN problem faced by the NOMA scheme.

Magnetic Resonance Imaging (MRI) is a crucial imaging modality for viewing internal body structures. This research analyzes the performance of popular GAN models for accurate and precise MRI reconstruction by enhancing image quality and improving diagnostic accuracy. Three GAN architectures considered in this study are Vanilla GAN, Deep Convolutional GAN (DCGAN), and Wasserstein GAN (WGAN). They were trained and evaluated using knee, brain, and cardiac MRI datasets to assess their generalizability across body regions. While the Vanilla GAN operates on the fundamentals of the adversarial network setup, DCGAN advances image synthesis by securing the convolutional layers, giving a superior appearance to the prevalent spatial features. Training instability is resolved in WGAN through the Wasserstein distance to minimize an unstable regime, therefore, ensuring stable convergence and high-quality images. The GAN models were trained and tested using 1000 MR images of an anonymized knee, 805 images of the heart, and 90 images of the brain MRI datasets. The Structural Similarity Index (SSIM) for Vanilla GAN is 0.84, DCGAN is 0.97, and WGAN is 0.99. The Peak Signal-to-Noise Ratio (PSNR) for Vanilla GAN is 26, DCGAN is 49.3, and WGAN is 43.5. The results were further statistically validated. This study shows that DCGAN and WGAN-based frameworks are promising in MR image reconstruction because of good image quality and superior accuracy. With the first cross-organ benchmark of baseline GANs under a common preprocessing pipeline, this work provides a reproducible benchmark for future hybrid GANs and clinical MRI applications.