Today, internet and device ubiquity are paramount in individual, formal and societal considerations. Next generation communication technologies, such as Blockchains (BC), Internet of Things (IoT), cloud computing, etc. offer limitless capabilities for different applications and scenarios including industries, cities, healthcare systems, etc. Sustainable integration of healthcare nodes (i.e. devices, users, providers, etc.) resulting in healthcare IoT (or simply IoHT) provides a platform for efficient service delivery for the benefit of care givers (doctors, nurses, etc.) and patients. Whereas confidentiality, accessibility and reliability of medical data are accorded high premium in IoHT, semantic gaps and lack of appropriate assets or properties remain impediments to reliable information exchange in federated trust management frameworks. Consequently, We propose a Blockchain Decentralised Interoperable Trust framework (DIT) for IoT zones where a smart contract guarantees authentication of budgets and Indirect Trust Inference System (ITIS) reduces semantic gaps and enhances trustworthy factor (TF) estimation via the network nodes and edges. Our DIT IoHT makes use of a private Blockchain ripple chain to establish trustworthy communication by validating nodes based on their inter-operable structure so that controlled communication required to solve fusion and integration issues are facilitated via different zones of the IoHT infrastructure. Further, C] implementation using Ethereum and ripple Blockchain are introduced as frameworks to associate and aggregate requests over trusted zones.

Melanoma is considered the most serious type of skin cancer. All over the world, the mortality rate is much high for melanoma in contrast with other cancer. There are various computer-aided solutions proposed to correctly identify melanoma cancer. However, the difficult visual appearance of the nevus makes it very difficult to design a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection. Existing systems either uses traditional machine learning models and focus on handpicked suitable features or uses deep learning-based methods that use complete images for feature learning. The automatic and most discriminative feature extraction for skin cancer remains an important research problem that can further be used to better deep learning training. Furthermore, the availability of the limited available images also creates a problem for deep learning models. From this line of research, we propose an intelligent Region of Interest (ROI) based system to identify and discriminate melanoma with nevus cancer by using the transfer learning approach. An improved k-mean algorithm is used to extract ROIs from the images. These ROI based approach helps to identify discriminative features as the images containing only melanoma cells are used to train system. We further use a Convolutional Neural Network (CNN) based transfer learning model with data augmentation for ROI images of DermIS and DermQuest datasets. The proposed system gives 97.9% and 97.4% accuracy for DermIS and DermQuest respectively. The proposed ROI based transfer learning approach outperforms existing methods that use complete images for classification.

Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.

Parkinson's disease is a neural degenerative disease. It slowly progresses from mild to severe stage, resulting in the degeneration of dopamine cells of neurons. Due to the deficiency of dopamine cells in the brain, it leads to a motor (tremor, slowness, impaired posture) and non-motor (speech, olfactory) defects in the body. Early detection of Parkinson's disease is a difficult chore as the symptoms of disease appear overtime. However, different diagnostic systems have contributed towards disease detection by considering gait, tremor and speech characteristics. Recent work has shown that speech impairments can be considered as a possible predictor for Parkinson's disease classification and remains an open research area. The speech signals show major differences and variations for Parkinson patients as compared to normal human beings. Therefore, variation in speech should be modeled using acoustic features to identify these variations. In this research, we propose three methods- the first method employs a transfer learning-based approach using spectrograms of speech recordings, the second method evaluates deep features extracted from speech spectrograms using machine learning classifiers and the third method evaluates simple acoustic feature of recordings using machine learning classifiers. The proposed frameworks are evaluated on a Spanish dataset pc-Gita. The results show that the second framework shows promising results with deep features. The highest 99.7% accuracy on vowel o and read text is observed using a multilayer perceptron. Whereas 99.1% accuracy observed on vowel i deep features using random forest. The deep feature-based method performs better as compared to simple acoustic features and transfer learning approaches. The proposed methodology outperforms the existing techniques on the pc-Gita dataset for Parkinson's disease detection.

Gastrointestinal (GI) tract diseases are on the rise in the world. These diseases can have fatal consequences if not diagnosed in the initial stages. WCE (wireless capsule endoscopy) is the advanced technology used to inspect gastrointestinal diseases such as ulcerative-colitis, polyps, esophagitis, and ulcers. WCE produces thousands of frames for a single patient’s procedure for which manual examination is tiresome, time-consuming, and prone to error; therefore, an automated procedure is needed. WCE images suffer from low contrast which increases inter-class and intra-class similarity and reduces the anticipated performance. In this paper, an efficient GI tract disease classification technique is proposed which utilizes an optimized brightness-controlled contrast-enhancement method to improve the contrast of the WCE images. The proposed technique applies a genetic algorithm (GA) for adjusting the values of contrast and brightness within an image by modifying the fitness function, which improves the overall quality of WCE images. This quality improvement is reported using qualitative measures, such as peak signal to noise ratio (PSNR), mean square error (MSE), visual information fidelity (VIF), similarity index (SI), and information quality index (IQI). As a second step, data augmentation is performed on WCE images by applying multiple transformations, and then, transfer learning is used to fine-tune a modified pre-trained model on WCE images. Finally, for the classification of GI tract disease, the extracted features are passed through multiple machine-learning classifiers. To show the efficacy of the proposed technique in the improvement in classification performance, the results are reported for the original dataset as well as the contrast-enhanced dataset. The results show an overall improvement of 15.26% in accuracy, 13.3% in precision, 16.77% in recall rate, and 15.18% in F-measure. Finally, a comparison with the existing techniques shows that the proposed framework outperforms the state-of-the-art techniques.

Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry out the review, six different databases are selected. Inclusion and exclusion criteria are employed to limit the research. Further, the eligible articles are classified depending on publication year, authors, type of articles, research objective, inputs and outputs, problem and research gaps, and findings and results. Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis. The findings of this study show the most used ML methods and the most common diseases that are focused on by researchers. It also shows the increase in use of machine learning for disease diagnosis over the years. These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.