Automated Deep Learning Empowered Breast Cancer Diagnosis UsingBiomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques.Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate.But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives.For resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital mammograms.The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms.The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation.In addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction purposes.Specifically,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 model.The wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast cancer.The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures.The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

A Feature Selection Strategy to Optimize Retinal Vasculature Segmentation

Diabetic retinopathy (DR) is a complication of diabetesmellitus thatappears in the retina. Clinitians use retina images to detect DR pathologicalsigns related to the occlusion of tiny blood vessels. Such occlusion brings adegenerative cycle between the breaking off and the new generation of thinnerand weaker blood vessels. This research aims to develop a suitable retinalvasculature segmentation method for improving retinal screening proceduresby means of computer-aided diagnosis systems. The blood vessel segmentationmethodology relies on an effective feature selection based on SequentialForward Selection, using the error rate of a decision tree classifier in theevaluation function. Subsequently, the classification process is performed bythree alternative approaches: artificial neural networks, decision trees andsupport vector machines. The proposed methodology is validated on threepublicly accessible datasets and a private one provided by Hospital Sant Joanof Reus. In all cases we obtain an average accuracy above 96% with a sensitivityof 72% in the blood vessel segmentation process. Compared with the state-ofthe-art, our approach achieves the same performance as other methods thatneed more computational power.Our method significantly reduces the numberof features used in the segmentation process from 20 to 5 dimensions. Theimplementation of the three classifiers confirmed that the five selected featureshave a good effectiveness, independently of the classification algorithm.

Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and others.Besides,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several diseases.Ovarian Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality rate.The OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate OC.In this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart healthcare.The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately.Besides,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data.In addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets.Moreover,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification performance.The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty.The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.