Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease.

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

Comparative analysis of high-intensity interval training and traditional rehabilitation programs for accelerated recovery from musculoskeletal injuries.

BACKGROUND: Musculoskeletal injuries, such as strains, are prevalent across all age groups and have a substantial impact on daily functioning and quality of life. OBJECTIVE: To examine the effectiveness of high-intensity interval training (HIIT) with traditional rehabilitation programs on pain, range of motion (ROM), muscular strength, and functional changes in promoting accelerated recovery from musculoskeletal injuries. METHODS: A total of 80 participants (54 males, 26 females; mean age 35.6 years) with various musculoskeletal injuries were randomly assigned to either the HIIT group (n= 40) or the traditional rehabilitation group (n= 40). The HIIT group underwent a six-week supervised program, with three sessions per week. The traditional rehabilitation group followed a similar six-week program emphasizing low to moderate intensity exercises and traditional rehabilitation techniques. Outcome measures, including pain levels, ROM, muscular strength, and functional outcomes, were assessed pre- and post-intervention. RESULTS: Significant improvements were observed in both the HIIT and traditional rehabilitation groups. However, the HIIT group demonstrated superior outcomes. Participants in the HIIT group experienced a greater reduction in pain levels compared to the traditional rehabilitation group (mean visual analog scale (VAS) score decrease of 5.2 vs. 3.8, respectively, p< 0.05). Functional outcomes significantly favored the HIIT group, with participants achieving faster completion times in the Timed Up and Go test (mean reduction of 2.1 seconds vs. 1.5 seconds, respectively, p< 0.01) and longer distances in the Single Leg Hop test (mean increase of 32 cm vs. 25 cm, respectively, p< 0.05). CONCLUSION: HIIT showed superior effectiveness over traditional rehabilitation in accelerating recovery from musculoskeletal injuries, with greater pain reduction and improved functional outcomes. Incorporating HIIT into rehabilitation protocols may offer an efficient approach for expedited recovery and enhanced functional capacity.

Heart disease severity level identification system on Hyperledger consortium network.

Electronic Health Records (EHRs) play a vital role in the healthcare domain for the patient survival system. They can include detailed information such as medical histories, medications, allergies, immunizations, vital signs, and more. It can help to reduce medical errors, improve patient safety, and increase efficiency in healthcare delivery. EHR approaches are proven to be an efficient and successful way of sharing patients' personal health information. These kinds of highly sensitive information are vulnerable to privacy and security associated threats. As a result, new solutions must develop to meet the privacy and security concerns in health information systems. Blockchain technology has the potential to revolutionize the way electronic health records (EHRs) are stored, accessed, and utilized by healthcare providers. By utilizing a distributed ledger, blockchain technology can help ensure that data is immutable and secure from tampering. In this article, a Hyperledger consortium network has been developed for sharing health records with enhanced privacy and security. The attribute based access control (ABAC) mechanism is used for controlling access to electronic health records. The use of ABAC on the network provides EHRs with an extra layer of security and control, ensuring that only authorized users have access to sensitive data. By using attributes such as user identity, role, and health condition, it is possible to precisely control access to records on blockchain. Besides, a Gaussian naïve Bayes algorithm has been integrated with this consortium network for prediction of cardiovascular disease. The prediction of cardiovascular is difficult due to its correlated risk factors. This system is beneficial for both patients and physicians as it allows physicians to quickly identify high-risk patients and easily provide them with patient severity level using feature weight prediction algorithms. Dynamic emergency access control privileges are used for the emergency team and will be withdrawn once the emergency has been resolved, depending on the severity score. The system is implemented with the following medical datasets: the heart disease dataset, the Pima Indian diabetes dataset, the stroke prediction dataset, and the body fat prediction dataset. The above datasets are obtained from the Kaggle repository. This system evaluates system performance by simulating various operations using the Hyperledger Caliper benchmarking tool. The performance metrics such as latency, transaction rate, resource utilization, etc. are measured and compared with the benchmark.