Automated gender recognition is an attractive research area in computer vision. It has many application areas, such as biometric, human-computer interaction, demographic statistics, retail shops, surveillance, etc. It aims to identify gender through the appearance, behavior, and other aspects of a person. Our proposed convolutional neural network (CNN)-based approach does not use raw facial images for learning the features. First, featured images are constructed from raw images to represent a set of salient features. Then, more salient features are extracted using CNN-based networks in which the featured images are given as input. Then, the extracted salient features are fused together and given as input to the classifier, support vector machine, for recognizing the gender. Our proposed novel framework is tested using Adience benchmark dataset and the stated functionality requirements are met. From this work, it is inferred that using a CNN-based approach with featured images as input, effective gender recognition can be done and the learned features are more amenable for effective classification. The framework is able to handle the faces that are captured in totally diverse environments. It can be applied to several visual pattern recognition problems. 

Gender recognition from facial images using multichannel deep learning framework

Emerging medical imaging applications in healthcare, the number and volume of medical images is growing dramatically. Information needs of users in such circumstances, either for clinical or research activities, make the role of powerful medical image search engines more significant. In this paper, a text-based multi-dimensional medical image indexing technique is proposed in which correlation of the features-usages (according to the user's queries) is considered to provide an off-the content indexing while taking users' interestingness into account. Assuming that each medical image has some extracted features (e.g., based on the DICOM standard), correlations of the features are discovered by performing data mining techniques (i.e., quantitative association pattern discovery), on the history of users' queries as a data set. Then, based on the pairwise correlation of the features of medical images (a.k.a. Affinity), set of the all features is fragmented into subsets (using method like the vertical fragmentation of the tables in distribution of relational DBs). After that, each of these subsets of the features turn into a hierarchy of the features (by applying a hierarchical clustering algorithm on that subset), subsequently all of these distinct hierarchies together make a multi-dimensional structure of the features of medical images, which is in fact the proposed text-based (feature-based) multi-dimensional index structure. Constructing and using such text-based multi-dimensional index structure via its specific required operations, medical image retrieval process would be improved in the underlying medical image search engine. Generally, an indexing technique is to provide a logical representation of documents in order to optimize the retrieval process. The proposed indexing technique is designed such that can improve retrieval of medical images in a medical image search engine in terms of its effectiveness and efficiency. Considering correlation of the features of the image would semantically improve precision (effectiveness) of the retrieval process, while traversing them through the hierarchy in one dimension would try to optimize (i.e., minimize) the resources to have a better efficiency. The proposed text-based multi-dimensional indexing technique is implemented using the open source search engine Lucene, and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier platform (available for the benchmarking of information retrieval systems) and other the most related indexing techniques. Evaluation results of memory usage and time complexity analysis, beside the experimental evaluations efficiency and effectiveness measures show that the proposed multi-dimensional indexing technique significantly improves both efficiency and effectiveness for a medical image search engine.

/pdf/text-based-multi-dimensional-medical-images-retrieval-yb3x5h6l8c.pdf

Text-based multi-dimensional medical images retrieval according to the features-usage correlation.

Content-based image retrieval with fuzzy clustering for feature vector normalization

Radiologists or other clinical professionals must spend a lot of time and effort segmenting, detecting, and extracting the infected tumor area from magnetic resonance (MR) images, and their accuracy is solely dependent on their experience. Computer-aided technology becomes essential to get beyond these constraints. Single layer network's feature information suffers from issues such as a lack of contextual information, which could make the model insensitive to small tumors and have poor detection accuracy. This research study proposes an MSF-YOLO model based on the YOLOv3 algorithm to address the abovementioned problems. The multi-scale aspects of the image are first combined. The single convolutional scale of the original ResNet cell is extended to four convolutional scales, and the characteristics under each distinct perceptual field are merged to get rich hierarchical information from images. Then the first anchor box is optimized. The size of the initial anchor box is optimized twice using K-means clustering techniques to increase the overlap of the anchor box and boost model accuracy. Finally, the model's convergence is sped up. The model's training procedure is optimized, and the model's convergence is hastened by introducing the weight parameters learned through training on the COCO dataset. Experimental results on two open datasets demonstrate that MSF-YOLO outperforms YOLOv3 with an average accuracy of 98.67% and 97.51 % and outperforms state-of-the-art models in mAP and IoU measures. 

Multi-Scale Features Fusion with YOLOv3 for Detecting Small and Fine Tumors in MRI images

Initially, blockchain technology was emerged for the enhancement of financial transactions, as it is independent of the need to have any third party to verify the transactions. Progressively, it has been slightly modified based on the different application-specific requirements such as data security and privacy. One of the emerging applications of blockchain is E-healthcare that concerns mainly about integrity, authenticity, and consistency of patients’ medical records. Due to the evolution of Internet of Things (IoT), a lot of healthcare data is being produced through the use of various devices like smart watches, smart sphygmomanometer, smart thermometer, etc. This imposes the need to concern about scalability along with interoperability. A novel architecture to handle the electronic medical records (EMR) of the patients by various medical entities is proposed. As large amounts of records are to be handled, the healthcare archives are kept in the cloud for streamlining the usage of information among diverse stakeholders. Also, there is a provision to enable the measures that handle security and privacy in the cloud architecture. Suitable public key cryptography and hashing methods are exploited to maintain the past transactions corresponding to the patients’ records. This preserves confidentiality, integrity, and availability. It also prevents the modification or falsification of data by unauthorized persons. Using blockchains, patients’ records can be added only at the end of the database, but they cannot be removed. New records are securely connected to the preceding record using cryptographic hashing. Special node called data validator is used to check the quality and authenticity of user-uploaded data, from which the records can be examined and patient health status reports are prepared. Again, encryption and digital signing are performed on the data to store it back to the blockchain. This proposed system ensures that no individual can modify or damage the verified records that are already stored. Our proposed novel architecture was tested against MIT-BIH Arrhythmia Database, and the stated functionality requirements are met.

Blockchain-Powered Healthcare Information Exchange Systems to Support Various Stakeholders

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on...

https://journals.sagepub.com/doi/pdf/10.1177/0020294018824122

Combined global and local semantic feature–based image retrieval analysis with interactive feedback:

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Combined global and local semantic feature–based image retrieval analysis with interactive feedback: