In this paper, the classification capability of Calinski-Harabasz criterion as an internal cluster validation measure has been evaluated for clustering-based region discrimination on cervical cells. In this approach, subregions in the sample image are initially randomly constructed to be the individuals of the population. At each generation, individuals are evaluated according to their Accordingly a novel genetic structure for meta heuristic area isolation is proposed. Evaluation of proposed combination of genetic algorithm and Calinski-Harabasz measure is achieved by experiments, conducted on real cervical cell samples. We have used two separate cluster validity measures to evaluate the performance of the clustering approach. Jaccard index and F-score are utilized for objective comparison. Results shows that, Calinski-Harabasz criteria may have a better performance with proposed novel genetic structure and presented mechanism may have great potential on discrimination of specific regions.

Purpose: Osteoarthritis is a serious condition that can significantly reduce a person’s quality of life, causing pain and stiffness in the knees and limiting their mobility. The condition progressively worsens over time, emphasizing the importance of early diagnosis. This study implemented a computer-aided classification approach to reduce the time and effort required for diagnosing knee osteoarthritis while minimizing human errors. Materials and Methods: Data analyzed in this study was obtained from the Osteoarthritis Initiative. A total of 165 samples were used in the study. All abnormal samples were graded as severe osteoarthritis. While 78 samples were used to test the implemented algorithm, the training process of the algorithm was completed with 87 samples. The proposed approach involves three main stages: segmenting the cartilage region through a series of image-processing operations, extracting morphological features from the defined region, and classifying samples based on these features. In the classification stage, morphological features characterizing the cartilage region were classified in the observation space, and the k-nearest neighbors algorithm was applied for automated discrimination. Accordingly, the computer utilizes the previously classified sample features to estimate the presence of pathology. Results: Test classifications were completed with 78 samples; 28 were previously diagnosed with osteoarthritis. Morphological measures of the training samples were accepted as a reference for abnormality. The applied classification scheme can distinguish severed cartilage regions with a 0.95% accuracy. Conclusion: This study demonstrates the potential effectiveness of a computer-aided approach in diagnosing knee osteoarthritis with high accuracy. The developed approach offers a promising solution for early and efficient diagnosis, enabling more timely and effective treatment strategies for osteoarthritis patients. The progressive nature of the disease makes these advancements in diagnostic methods invaluable. Future studies may focus on expanding the sample size and further refining the model for enhanced precision and broad applicability in clinical settings.

Fish contains many nutrients beneficial to human health, which makes fish an essential component of a healthy diet. Omega-3 fatty acids, primarily found in fresh fish, can play a critical role in protecting heart and brain health. Freshness is one of the most important quality criteria of the fish to be selected for consumption. It is known that there may be pathogenic bacteria and toxins to human health in fish that are not stored in the right conditions and transferred by wrong logistics methods. One of the widely used approaches for evaluating the freshness of fish is sensory, which would be highly subjective and error-prone. Moreover, sensory analysis is widespread and one of the fastest approaches for evaluating large quantities of fish. At that point, a computer-aided diagnostic system can accelerate the evaluation of the degree of spoilage, reduce the human resources required for this task, and minimize the possibility of spoiled fish consumption. In this study, a fully automated freshness assessment mechanism based on the analysis of digital eye images of fish is proposed. Accordingly, the unsupervised clustering approach was used for feature extraction, and each image was divided into three regions according to their color distribution. The freshness was evaluated according to the intensity difference between these clusters. The results show that the proposed feature extraction approach is highly distinctive for the discrimination of spoilage and can be used to distinguish fresh fish from spoiled fish using machine learning methods without supervision.

Background: Skin aging can be observed at various levels in the epidermis, dermis, and dermo‐epidermal junction. Reducing the cosmetic effects of skin aging in the facial region is a widespread demand due to common aesthetic concerns. Consequently, many injectable products on the market promise antiaging effects and cosmetic improvements. We aimed to evaluate the cosmetic efficacy of a high molecular weight sodium hyaluronate and amino acids mixture for the facial region using morphometric analysis.Methods: This study evaluates the morphometric effectiveness of an injectable mixture (high molecular weight sodium hyaluronate, glycine, L‐Proline, L‐leucine, L‐lysine HCL, L‐valine, and L‐alanine collagen active ingredient) on the mid‐face and jawline in women aged 30–55. We used morphological measurements and digital image data to assess changes and determine effectiveness. Various computational methods were applied simultaneously with statistical tests for validation.Results: The hydration assessment showed a significant increase on both sides of the face. A noticeable decrease was observed in gonion angle, bitragion breadth, bigonion breadth, and marionette wrinkle scale. These results suggest combining mechanical and chemical stimulation from the injection and its components (hyaluronic and amino acids) effectively enhances skin quality.Conclusions: The study indicates that the mechanical stimulation of the injection improves skin quality. Combining hyaluronic and amino acids (collagen, elastin, and pro‐synthetic) is a safe and effective alternative for antiaging treatments.