Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images

Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.

3D Convolutional Neural Network Framework with Deep Learning for Nuclear Medicine

Though artificial intelligence (AI) has been used in nuclear medicine for more than 50 years, more progress has been made in deep learning (DL) and machine learning (ML), which have driven the development of new AI abilities in the field. ANNs are used in both deep learning and machine learning in nuclear medicine. Alternatively, if 3D convolutional neural network (CNN) is used, the inputs may be the actual images that are being analyzed, rather than a set of inputs. In nuclear medicine, artificial intelligence reimagines and reengineers the field's therapeutic and scientific capabilities. Understanding the concepts of 3D CNN and U-Net in the context of nuclear medicine provides for a deeper engagement with clinical and research applications, as well as the ability to troubleshoot problems when they emerge. Business analytics, risk assessment, quality assurance, and basic classifications are all examples of simple ML applications. General nuclear medicine, SPECT, PET, MRI, and CT may benefit from more advanced DL applications for classification, detection, localization, segmentation, quantification, and radiomic feature extraction utilizing 3D CNNs. An ANN may be used to analyze a small dataset at the same time as traditional statistical methods, as well as bigger datasets. Nuclear medicine's clinical and research practices have been largely unaffected by the introduction of artificial intelligence (AI). Clinical and research landscapes have been fundamentally altered by the advent of 3D CNN and U-Net applications. Nuclear medicine professionals must now have at least an elementary understanding of AI principles such as neural networks (ANNs) and convolutional neural networks (CNNs).

Enriched Optimization Algorithm for Effective Skin Disease Prediction Using Soft Computing Techniques

In recent years, a usual worldwide problem is a skin disease—the diagnostics of infection and skin disease prediction based on the data mining techniques. The precise and cost-effective treatments obtain a technologybased data mining system that can consider making the right decision. Depends on data, there are 34 UCI datasets have in the skin disease prediction. All of the datasets are not much important when predicting the skin disease problem. In this study, the essential datasets to be analyzed because they only give the best accuracy in skin disease prediction. For an outstanding selection of allocation, to propose a novel feature selection approaches, Enriched Fruitfly Optimization Algorithm (EFOA), and Ensemble Classifiers that are helps for an early stage of skin disease prediction. A hybrid technique through the three essential hybrid feature selection approaches such as Chi-Square method, Information Gain method, and Principal Component Analysis (PCA) methods that are combined for better feature selection results. Based on the skin disease dataset, the resultant feature selection approach generated the reduced data subset. Then, the Enriched Fruitfly Optimization Algorithm (EFOA) offers the optimization of reduced data subset. Here, the accuracy estimation is the vital factor to optimize the effective and best prediction of skin disease affected regions. Afterward, the classification performs to classify the EFOA based optimized result by using the six different classification methods. Where, the classification helps to analyze the optimized results, which offers the better classification procedure. To predict the base learner’s performance, to utilize the Naive Bayesian, K-Nearest Neighbour, Decision Tree, Support Vector Machine, Random Forest, and Multilayer Perceptron (MLP) to classify the optimized result. Then, the ensemble techniques used to analyze the classifier’s results through the 3 different methods like Bagging, Boosting, Stacking, added on the base learners to improve the proposed work. Based on the performance, the base learners’ performance is larger than the input dataset. The base learner’s parameters are essential to calculate the accuracy of skin disease prediction performance. The performance of the proposed method will take and compare to each base learner, and the performance shows the accurate skin disease prediction improvement with other existing methods.

Aggregation of Region-based and Boundary-based Knowledge Biased Segmentation for Osteoporosis Detection from X-Ray, Dual X-Ray and CT Images

BACKGROUND

Osteoporosis is a term used to represent the reduced bone density, which is caused by insufficient bone tissue production to balance the old bone tissue removal. Medical Imaging procedures such as X-Ray, Dual X-Ray and Computed Tomography (CT) scans are used widely in osteoporosis diagnosis. There are several existing procedures in practice to assist osteoporosis diagnosis, which can operate using a single imaging method.

OBJECTIVE

The purpose of this proposed work is to introduce a framework to assist the diagnosis of osteoporosis based on consenting all these X-Ray, Dual X-Ray and CT scan imaging techniques. The proposed work named "Aggregation of Region-based and Boundary-based Knowledge biased Segmentation for Osteoporosis Detection from X-Ray, Dual X-Ray and CT images" (ARBKSOD) is the integration of three functional modules.

METHODS

Fuzzy Histogram Medical Image Classifier (FHMIC), Log-Gabor Transform based ANN Training for osteoporosis detection (LGTAT) and Knowledge biased Osteoporosis Analyzer (KOA).

RESULTS

Together, all these three modules make the proposed method ARBKSOD scored the maximum accuracy of 93.11%, the highest precision value of 93.91% while processing the 6th image batch, the highest sensitivity of 92.93%, the highest specificity of 93.79% is observed during the experiment by ARBKSOD while processing the 6th image batch. The best average processing time of 10244 mS is achieved by ARBKSOD while processing the 7th image batch.

CONCLUSION

Together, all these three modules make the proposed method ARBKSOD to produce a better result.