Breast cancer is a disease that affects women in different countries around the world. The real cause of breast cancer is particularly challenging to determine, and early detection of the disease is necessary for reducing the death rate, due to the high risks associated with breast cancer. Treatment in the early period can increase the life expectancy and quality of life for women. CAD (Computer Aided Diagnostic) systems can perform the diagnosis of the benign and malignant lesions of breast cancer using technologies and tools based on image processing, helping specialist doctors to obtain a more precise point of view with fewer processes when making their diagnosis by giving a second opinion. This study presents a novel CAD system for automated breast cancer diagnosis. The proposed method consists of different stages. In the preprocessing stage, an image is segmented, and a mask of a lesion is obtained; during the next stage, the extraction of the deep learning features is performed by a CNN—specifically, DenseNet 201. Additionally, handcrafted features (Histogram of Oriented Gradients (HOG)-based, ULBP-based, perimeter area, area, eccentricity, and circularity) are obtained from an image. The designed hybrid system uses CNN architecture for extracting deep learning features, along with traditional methods which perform several handcraft features, following the medical properties of the disease with the purpose of later fusion via proposed statistical criteria. During the fusion stage, where deep learning and handcrafted features are analyzed, the genetic algorithms as well as mutual information selection algorithm, followed by several classifiers (XGBoost, AdaBoost, Multilayer perceptron (MLP)) based on stochastic measures, are applied to choose the most sensible information group among the features. In the experimental validation of two modalities of the CAD design, which performed two types of medical studies—mammography (MG) and ultrasound (US)—the databases mini-DDSM (Digital Database for Screening Mammography) and BUSI (Breast Ultrasound Images Dataset) were used. Novel CAD systems were evaluated and compared with recent state-of-the-art systems, demonstrating better performance in commonly used criteria, obtaining ACC of 97.6%, PRE of 98%, Recall of 98%, F1-Score of 98%, and IBA of 95% for the abovementioned datasets.

The principles of the transform stage of the extract, transform and load (ETL) process can be applied to index the data in functional structures for the decision-making inherent in an urban remote sensing application. This work proposes a method that can be utilised as an organisation stage by reducing the data dimension with Gabor texture features extracted from grey-scale representations of the Hue, Saturation and Value (HSV) colour space and the Normalised Difference Vegetation Index (NDVI). Additionally, the texture features are reduced using the Linear Discriminant Analysis (LDA) method. Afterwards, an Artificial Neural Network (ANN) is employed to classify the data and build a tick data matrix indexed by the belonging class of the observations, which could be retrieved for further analysis according to the class selected to explore. The proposed method is compared in terms of classification rates, reduction efficiency and training time against the utilisation of other grey-scale representations and classifiers. This method compresses up to 87% of the original features and achieves similar classification results to non-reduced features but at a higher training time.

Ischemic stroke lesion segmentation in MRI images represents significant challenges, particularly due to class imbalance between foreground and background pixels. Several approaches have been developed to achieve higher F1-Scores in stroke lesion segmentation under this challenge. These strategies include convolutional neural networks (CNN) and models that represent a large number of parameters, which can only be trained on specialized computational architectures that are explicitly oriented to data processing. This paper proposes a lightweight model based on the U-Net architecture that handles an attention module and the Generalized Dice Focal loss function to enhance the segmentation accuracy in the class imbalance environment, characteristic of stroke lesions in MRI images. This study also analyzes the segmentation performance according to the pixel size of stroke lesions, giving insights into the loss function behavior using the public ISLES 2015 and ISLES 2022 MRI datasets. The proposed model can effectively segment small stroke lesions with F1-Scores over 0.7, particularly in FLAIR, DWI, and T2 sequences. Furthermore, the model shows reasonable convergence with their 7.9 million parameters at 200 epochs, making it suitable for practical implementation on mid and high-end general-purpose graphic processing units.

Methods of authentication and self-recovery of tampered information in digital images have been in constant development during the last years. These frameworks are developed by watermarking techniques, which generate an image digest, which is embedded inside the image that must be protected. In this paper, a method of authentication and self-recovery of tampered information in digital images is proposed. Our approach first generates the authentication watermarks, which are based on XOR operations on non-overlapping blocks, subsequently by using a halftoning technique the recovery watermark is generated. To have a higher performance, three copies of each watermark are embedded to have more chance to extract the recovery watermark. To evaluate the quality of the obtained images, the objective criterion of peak signal-to-noise ratio (PSNR) and Structural Similarity Index (SSIM) are used. The experimental results demonstrate the effectiveness of our method in comparisons with other schemes reported in the literature, where the quality of the watermarked images, the quality of the reconstruction images and the recovery rate of each scheme were evaluated.

In this paper, we propose a robust digital video watermarking scheme with completely blind extraction process where the original video data, original watermark or any other information derivative of them are not required in order to retrieve the embedded watermark. The proposed algorithm embeds 2D binary visually recognizable patterns such as company trademarks and owner’s logotype, etc., in the DWT domain of the video frames for copyright protection.Before the embedding process, only two numerical keys are required to transform the watermark data into a noise-like pattern using the chaotic mixing method which helps to increase the security. The main advantages of the proposed scheme are its completely blind detection scheme, robustness against common video attacks, combined attacks andits low complexity implementation. The combined attacks consist of MPEG-2 compression and common video attacks such as noise contamination, collusion attacks, frame dropping and swapping. Extensive simulation results also show that the watermark imperceptibility and robustness outperform other previously reported methods. The extractedwatermark data from the watermarked video sequences is clear enough even after the watermarked video hadsuffered from several attacks. En este artículo se propone un sistema de marca de agua robusto para la protección de derechos de autor en video digital donde el proceso de extracción de la marca de agua es completamente a ciegas, ya que no requiere ninguna información directa o derivada del video original ni de la marca de agua original. El algoritmo inserta patrones binarios bidimensionales reconocibles visualmente, los cuales pueden ser logotipos de compañías o cualquier otra imagen en el dominio de la DWT de algunos cuadros de video seleccionados aleatoriamente. Antes del proceso de inserción, son necesarias únicamente dos llaves para desordenar los datos de la marca de agua mediante el método de mezclas caóticas, con la finalidad de incrementar la seguridad. Las ventajas principales del sistema propuesto son la extracción de la marca de agua es completamente a ciegas, su bajo costo computacional y su desempeño en contra de ataques combinados al video es mejor que otros métodos propuestos por diferentes autores. Resultados obtenidos demuestran la imperceptibilidad de la marca de agua y que la marca de agua extraída es suficientemente distinguible aún después de que la secuencia de video ha sido sometida a diversos ataques combinados.