RETRACTED: Lung cancer diagnosis of CT images using metaheuristics and deep learning.

Lung cancer is the uncontrolled growth of cells in the lung that is made up of two spongy organs located in the chest. These cells may penetrate outside the lung in a process called metastasis and spread to the tissues and organs of the body and increase the risk of death from this disease. CT scan of pulmonary nodules is one of the methods of early detection of lung cancer. One of the main challenges in diagnosing pulmonary nodules is the difficulty of identifying and distinguishing pulmonary nodules from lung components. In this study, a computer-aided detection system is introduced to identify these nodules. In the study, after image preprocessing, an image segmentation based on Otsu followed by mathematical morphology is proposed. Then, optimal features are selected based on a new metaheuristic method. Consequently, the characteristics are injected into an improved convolutional neural network (CNN)-based classifier to provide a high accuracy diagnosis system. The optimization of the Otsu method, feature selection, and CNN classifier is established by a new modified version of the Red Fox Optimizer (RFO) algorithm. The approach is then applied to three popular lung cancer datasets and the results are compared with three state-of-the-art methods to show the proposed method's higher efficiency.

Optimal diagnosis of the skin cancer using a hybrid deep neural network and grasshopper optimization algorithm.

When skin cells divide abnormally, it can cause a tumor or abnormal lymph fluid or blood. The masses appear benign and malignant, with the benign being limited to one area and not spreading, but some can spread throughout the body through the body's lymphatic system. Skin cancer is easier to diagnose than other cancers because its symptoms can be seen with the naked eye. This makes us to provide an artificial intelligence-based methodology to diagnose this cancer with higher accuracy. This article proposes a new non-destructive testing method based on the AlexNet and Extreme Learning Machine network to provide better results of the diagnosis. The method is then optimized based on a new improved version of the Grasshopper optimization algorithm (GOA). Simulation of the proposed method is then compared with some different state-of-the-art methods and the results showed that the proposed method with 98% accuracy and 93% sensitivity has the highest efficiency.

RETRACTED: Skin cancer diagnosis based on a hybrid AlexNet/extreme learning machine optimized by Fractional-order Red Fox Optimization algorithm.

Skin cancer is known as one of the most usual malignant cancer in the human body. Statistics show that each year above one million people are added to the who has this cancer. There are different types of skin cancer, where, the main difference is on the type of cell that is developing cancer. The best way to treat this cancer is to diagnose it early and to prevent the lesion from spreading with surgery. Early detection and treatment of skin cancer from skin images can significantly reduce mortality. Although skin cancer is very dangerous, early diagnosis and appropriate treatment, in most cases, prevent death. The present study introduces a new diagnostic technique for skin cancer based on deep learning and metaheuristics. At first, a pre-trained modified AlexNet based on batch normalization layers is used to train the skin dermoscopy images. Afterward, the last several layers are substituted by an Extreme Learning Machine (ELM). For providing higher efficiency in the ELM network, a newly amended metaheuristic, called Fractional-order Red Fox Optimization (FORFO) Algorithm is used. The final results of the proposed technique are compared with some various techniques and the results showed the effectiveness of the suggested method.

Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm.

BACKGROUND: In this paper, a new method for optimal enhancement of the contrast of a medical image is proposed. The main idea is to improve the Gamma correction method to enhance and highlight the image information and the details based on a new design of the World Cup Optimization (WCO) algorithm. Gamma correction is a suitable method for contrast enhancement with an efficiency that directly depends on the correct selection of the Gamma coefficient. METHODS: In this study, a newly presented algorithm was employed for optimal selection of the Gamma value by considering the entropy, edge content, and multi-objective optimization. RESULTS: The simulation results were compared with 5 state of the art methods for presenting method efficiency. To do this, contrast, homogeneity, weighted average peak signal-to-noise ratio (WPSNR), measure of enhancement (EME), and contrast-to-noise ratio (CNR) were employed. CONCLUSIONS: Final results denote that the presented multi-objective optimization algorithm improves the quality of the image contrast and can provide more details and information than the other comparable methods.

One-pot synthesis of 2-amino-4H-chromene derivatives by MNPs@Cu as an effective and reusable magnetic nanocatalyst.

In this research, MNPs@Cu as an effective and recyclable nanocatalyst was prepared and characterized using different methods including Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), vibrating sample magnetometry (VSM), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). After the characterization of this new nanocatalyst, it was efficiently used for the promotion of the one-pot synthesis of 2-amino-4H-chromene derivatives via one-pot three-component reaction of the enolizable compound, malononitrile, and arylaldehydes under solvent-free conditions at 90 °C. The procedure gave the desired products in high-to-excellent yields in short reaction times. Also this catalyst, because of its magnetic nature, can be simply restored by a permanent magnetic field and comfortably reused several times without any significant loss of its catalytic activity.