A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images.

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease's influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue's nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework's higher accuracy and precision rate.

An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm.

Skin cancer is one of the worst cancers nowadays that poses a severe threat to the health and safety of individuals. Therefore, skin cancer classification and early diagnosis are recommended to preserve human life. Multilevel thresholding image segmentation is well-known and influential technique for extracting regions of interest from skin cancer images to improve the classification process. Therefore, this paper proposes an efficient version of the recently developed golden jackal optimization (GJO) algorithm, the opposition-based golden jackal optimizer (IGJO). The IGJO algorithm is used to solve the multilevel thresholding problem using Otsu's method as an objective function. The proposed algorithm is compared with seven other meta-heuristic algorithms: whale optimization algorithm, seagull optimization algorithm, salp swarm algorithm, Harris hawks optimization, artificial gorilla troops optimizer, marine predators' algorithms, and original GJO algorithm. The performance of the proposed algorithm is evaluated using four popular performance measures: peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error. Experimental results show that the proposed algorithm outperforms other alternative algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics and effectively resolves the segmentation problem.

Stent for Life Initiative placed at the forefront in Egypt 2011.

AIMS: Myocardial infarction is a medical emergency in which 25 to 35% of patients will die before receiving medical attention. The Stent for Life registry was launched to access the current situation of the Egyptian population presenting with STEMI, and to determine what were the barriers to providing patients with cardiac problems appropriate care. METHODS AND RESULTS: This registry was conducted at 14 centres covered all the Egyptian regions including 1,324 consecutive patients presenting with STEMI during the period between January 1st, 2011 to November, 2011. Fourteen centres and 38 interventionalists participated in this registry; only six centres are Pilot Centres (fulfilling the criteria for a primary PCI centre) and were assigned at the end of 2011. Cardiovascular risk factors were mainly smoking (60.5%), dyslipidaemia (46.0%), diabetes (51.4%) and hypertension (56.0%). The mean age at presentation was 56.01±10.61years and 75.0% were male. Only 5% of the STEMI patients arrived via the emergency medical system. Thrombus aspiration was done in 42.7% of patients in primary PCI group and 25.9% in rescue PCI group. Bare metal stents (BMS) were used in 80.7% of the stented patients while drug eluting stents (DES) were used in 19.3% of the stented patients. In-hospital mortality was 2.9% (1.4% in primary PCI group, 1.1% in patients treated with thrombolysis and 0.4% in patients receiving no reperfusion therapy). CONCLUSION: Despite the logistical difficulties, excellent outcomes for acute interventional reperfusion strategy in STEMI can be achieved in our country, possibly similar to those seen in the West. There is a strong need for making the practice of PCI in STEMI more widespread in developing regions.