Brain tumor recognition by an optimized deep network utilizing ammended grasshopper optimization.

Brain tumors are abnormal cell masses that can get originated in the brain spread from other organs. They can be categorized as either malignant (cancerous) or benign (noncancerous), and their growth rates and locations can impact the functioning of the nerve system. The timely detection of brain tumors is crucial for effective treatment and prognosis. In this study, a new approach has been proposed for diagnosing brain tumors using deep learning and a meta-heuristic algorithm. The method involves three main steps: (1) extracting features from brain MRI images using AlexNet, (2) reducing the complexity of AlexNet by employing an Extreme Learning Machine (ELM) network as a classification layer, and (3) fine-tuning the parameters of the ELM network using an Amended Grasshopper Optimization Algorithm (AGOA). The performance of the method has been evaluated on a publicly available dataset consisting of 20 patients with newly diagnosed glioblastoma that is compared with several state-of-the-art techniques. Experimental results demonstrate that the method achieves the highest accuracy, precision, specificity, F1-score, sensitivity, and MCC with values of 0.96, 0.94, 0.96, 0.96, 0.94, and 0.90, respectively. Furthermore, the robustness and stability of the method have been illustrated when subjected to different levels of noise and image resolutions. The proposed approach offers a rapid, accurate, and dependable diagnosis of brain tumors and holds potential for application in other medical image analysis tasks.

Boosted dipper throated optimization algorithm-based Xception neural network for skin cancer diagnosis: An optimal approach.

Skin cancer is a prevalent form of cancer that necessitates prompt and precise detection. However, current diagnostic methods for skin cancer are either invasive, time-consuming, or unreliable. Consequently, there is a demand for an innovative and efficient approach to diagnose skin cancer that utilizes non-invasive and automated techniques. In this study, a unique method has been proposed for diagnosing skin cancer by employing an Xception neural network that has been optimized using Boosted Dipper Throated Optimization (BDTO) algorithm. The Xception neural network is a deep learning model capable of extracting high-level features from skin dermoscopy images, while the BDTO algorithm is a bio-inspired optimization technique that can determine the optimal parameters and weights for the Xception neural network. To enhance the quality and diversity of the images, the ISIC dataset is utilized, a widely accepted benchmark system for skin cancer diagnosis, and various image preprocessing and data augmentation techniques were implemented. By comparing the method with several contemporary approaches, it has been demonstrated that the method outperforms others in detecting skin cancer. The method achieves an average precision of 94.936%, an average accuracy of 94.206%, and an average recall of 97.092% for skin cancer diagnosis, surpassing the performance of alternative methods. Additionally, the 5-fold ROC curve and error curve have been presented for the data validation to showcase the superiority and robustness of the method.

Correction to: Emerging roles of JMJD3 in cancer

Histone lysine methylation plays a key role in gene activation and repression. The trimethylation of histone H3 on lysine-27 (H3K27me3) is a critical epigenetic event that is controlled by Jumonji domain-containing protein-3 (JMJD3). JMJD3 is a histone demethylase that specifically removes methyl groups. Previous studies have suggested that JMJD3 has a dual role in cancer cells. JMJD3 stimulates the expression of proliferative-related genes and increases tumor cell growth, propagation, and migration in various cancers, including neural, prostate, ovary, skin, esophagus, leukemia, hepatic, head and neck, renal, lymphoma, and lung. In contrast, JMJD3 can suppress the propagation of tumor cells, and enhance their apoptosis in colorectal, breast, and pancreatic cancers. In this review, we summarized the recent advances of JMJD3 function in cancer cells. (AU)

Emerging roles of JMJD3 in cancer

Histone lysine methylation plays a key role in gene activation and repression. The trimethylation of histone H3 on lysine-27 (H3K27me3) is a critical epigenetic event that is controlled by Jumonji domain-containing protein-3 (JMJD3). JMJD3 is a histone demethylase that specifically removes methyl groups. Previous studies have suggested that JMJD3 has a dual role in cancer cells. JMJD3 stimulates the expression of proliferative-related genes and increases tumor cell growth, propagation, and migration in various cancers, including neural, prostate, ovary, skin, esophagus, leukemia, hepatic, head and neck, renal, lymphoma, and lung. In contrast, JMJD3 can suppress the propagation of tumor cells, and enhance their apoptosis in colorectal, breast, and pancreatic cancers. In this review, we summarized the recent advances of JMJD3 function in cancer cells.

Emerging roles of JMJD3 in cancer.

Histone lysine methylation plays a key role in gene activation and repression. The trimethylation of histone H3 on lysine-27 (H3K27me3) is a critical epigenetic event that is controlled by Jumonji domain-containing protein-3 (JMJD3). JMJD3 is a histone demethylase that specifically removes methyl groups. Previous studies have suggested that JMJD3 has a dual role in cancer cells. JMJD3 stimulates the expression of proliferative-related genes and increases tumor cell growth, propagation, and migration in various cancers, including neural, prostate, ovary, skin, esophagus, leukemia, hepatic, head and neck, renal, lymphoma, and lung. In contrast, JMJD3 can suppress the propagation of tumor cells, and enhance their apoptosis in colorectal, breast, and pancreatic cancers. In this review, we summarized the recent advances of JMJD3 function in cancer cells.