Clinical imaging techniques have been widely used in the classification of cancer biopsy specimen histopathology images of limb soft tissue sarcoma (STS). Here, by automatically differentiating cell patterns in malignant and non-malignant tumors, an efficient classifier based on both accuracy and time requirements is significantly improved, which further reduces intra-inter-observer variations. Color normalization is carried out using a linear transformation into a grayscale image and the region of interest (ROI) of the image is selected by the pathologist. The wavelet transform has been used to extract statistical texture features (SFT) from the grayscale image of this ROI, and neural correlates with extracted features networks were trained For the purpose test, the features of a new limb STS tissue sample image are extracted and these extracted values are presented to the already trained networks for classification. In this case, the proposed research uses an artificial neural network (ANN), which leads to prominence by improving the classification methods based on accuracy, sensitivity and specificity. Here, two different types of ANN classifiers are discussed with back propagation neural network (BPNN) and radial basis function network (RBFN) classifiers. Furthermore, here the most significant difference between BPNN and RBFN is analyzed using the receiver operating characteristics (ROC) area under the curve. The performance accuracy of these two classification methods reaches 96.36% and 90.91 % for RBFN and BPNN, respectively. Based on these accuracy values, RBFN is found to be more efficient than BPNN classifiers. Finally the cancer cell classification accuracy is increased, decision- making time is reduced, and the initial treatment plan for chronic disease of the limbs tumor has been achieved

Digital clinical histopathology technique is used for accurately diagnosing cancer cells and achieving optimal results using Internet of Things (IoT) and blockchain technology. The cell pattern of Synovial Sarcoma (SS) cancer images always appeared as spindle shaped cell (SSC) structures. Identifying the SSC and its prognostic indicator are very crucial problems for computer aided diagnosis, especially in healthcare industry applications. A constructive framework has been proposed for the classification of SSC feature components using Support Vector Machine (SVM) with the assistance of relevant Support Vectors (SVs). This framework used the SS images, and it has been transformed into frequency sub-bands using Discrete Wavelet Transform (DWT). The sub-band wavelet coefficients of SSC and other Structure Features (SF) are extracted using Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA) techniques. Here, the maximum and minimum margin between hyperplane values of the kernel parameters are adjusted periodically as a result of storing the SF values of the SVs in the IoT devices. The performance characteristics of internal cross-validation and its statistical properties are evaluated by cross-entropy measures and compared by nonparametric Mann-Whitney U test. The significant differences in classification performance between the techniques are analyzed using the receiver operating characteristics (ROC) curve. The combination of QDA + SVM technique will be required for intelligent cancer diagnosis in the future, and it gives reduced statistic parameter feature set with greater classification accuracy. The IoT network based QDA + SVM classification technique has led to the improvement of SS cancer prognosis in medical industry applications using blockchain technology.

Atrial Fibrillation (AF) is a complex most prevalent arrhythmia with many complications that affect the affected individual’s overall health as his/her propensity to stroke and heart failure. This study seeks to investigate the effectiveness of Deep Residual Networks (ResNet) in the categorisation of AF from ECG signals given that this will help improve the AF diagnostic accuracy and speed. The ResNet model is based on deep architecture and uses residual connections to learn various patterns from ECG data. The process includes data preprocessing: filtering, normalization and seg-mentation; model training and testing. The results show a high accuracy of the method, the sensitivity, and specificity, which is higher than in many traditional and some modern techniques. The use of ResNet-based approach suggested for automated AF detections seems to be promising with its clinical application and the consequent effects on patients technology. Improvements for future work may be made to fine-tune the model and the integration of the proposed solution with other diagnostic aids for easier diagnosis of arrhythmias.

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.

Pathological examination is important for an accurate diagnosis of Synovial Sarcoma (SS). It is the most common cancer of the soft tissues of the limb in adolescents and adults. In this work, SS was used to determine the discriminant singularity characteristics using Wavelet Leaders (WL). The most popular technique for measuring the discriminant singularity characteristics of an image signal is the Lipschitz Exponent (LE). The singularity measurement was based on LE function by taking a slope of logarithmic scale versus logarithmic Wavelet Transform Modulus Maxima (WTMM). Here, the presence of the singularity was measured using WTMM and WL by summing each color component of an image signal. The performance characteristics of the statistical discrimination are evaluated and compared with the non-parametric hypothesis using the Wilcoxon rank-sum test. The most important difference between WTMM and WL was analyzed using the Receiver Operating Characteristics (ROC) curve. From the experimental analysis that the WL method provides excellent discriminant singularities or discontinuities performance characteristics such as area, standard error, z-statistics, and p-values. Finally, the results of experiments have proven that a WL can express practical, precise, robust, and satisfactory performance in practice.