Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False‐negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman’s premature death from the disease. False‐positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time‐consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c‐means approach is applied. The images are segmented using the fuzzy c‐means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.

Cancer is one of the vital diseases which lead to the uncontrollable growth of the cell, and it affects the body tissue. A type of cancer that affects the children below five years and adults in a rare case is called retinoblastoma. It affects the retina in the eye and the surrounding region of eye like the eyelid, and sometimes, it leads to vision loss if it is not diagnosed at the early stage. MRI and CT are widely used scanning procedures to identify the cancerous region in the eye. Current screening methods for cancer region identification needs the clinicians’ support to spot the affected regions. Modern healthcare systems develop an easy way to diagnose the disease. Discriminative architectures in deep learning can be viewed as supervised deep learning algorithms which use classification/regression techniques to predict the output. A convolutional neural network (CNN) is a part of the discriminative architecture which helps to process both image and text data. This work suggests the CNN‐based classifier which classifies the tumor and nontumor regions in retinoblastoma. The tumor‐like region (TLR) in retinoblastoma is identified using the automated thresholding method. After that, ResNet and AlexNet algorithms are used to classify the cancerous region along with classifiers. In addition, the comparison of discriminative algorithm along with its variants is experimented to produce the better image analysis method without the intervention of clinicians. The experimental study reveals that ResNet50 and AlexNet yield better results compared to other learning modules.

Introduction: Quercetin, a polyphenolic flavonoid that is abundant in fruits and vegetables, demonstrates substantial antioxidant, anti-inflammatory, immunomodulatory, and anti-cancer properties. It has garnered attention for its therapeutic potential in tuberculosis (TB), melanoma cancer, and Alzheimer's disease (AD). Methods: Following PRISMA 2020 guidelines, a systematic review was conducted using PubMed, Scopus, and Web of Science databases (2000-2024). The focus was on approaches to improve bioavailability, with an examination of preclinical models, pharmacological mechanisms, and therapeutic outcomes. Results: Quercetin reduced β-amyloid aggregation (45-60%), improved cognitive performance by up to 50%, and mitigated oxidative stress by nearly 50% in Alzheimer's models. In melanoma, it promoted apoptosis, inhibited angiogenesis (45% reduction), and decreased tumor volume by 40-60% in preclinical studies. In TB models, quercetin enhanced macrophage autophagy (30% increase), decreased bacterial burden by 40-60% and synergistically improved the efficacy of rifampicin by 35-40%. Addressing its bioavailability challenge (<1% in native form), nanotechnology-based delivery systems increased quercetin's absorption up to 10-fold, with certain systems achieving absolute bioavailability improvements of 30-35%. Discussion: Preclinical findings consistently highlight quercetin’s multitargeted role in modulating oxidative stress, inflammation, apoptosis, and immune responses across diverse disease models. However, discrepancies between experimental efficacy and clinical applicability are primarily due to its low systemic availability. Nanocarrier-based strategies, including liposomes, nanoparticles, and phytosomes, provide encouraging solutions, yet require robust clinical validation. Conclusion: Quercetin demonstrates multifaceted therapeutic potential across AD, melanoma, and TB. However, clinical translation remains limited by poor bioavailability and a lack of large-scale clinical validations. Future directions should emphasize advanced drug delivery systems, combination therapies, and robust clinical trials to establish quercetin's role as a potent therapeutic agent.