Early Melanoma Detection Based on a Hybrid YOLOv5 and ResNet Technique.

Skin cancer, specifically melanoma, is a serious health issue that arises from the melanocytes, the cells that produce melanin, the pigment responsible for skin color. With skin cancer on the rise, the timely identification of skin lesions is crucial for effective treatment. However, the similarity between some skin lesions can result in misclassification, which is a significant problem. It is important to note that benign skin lesions are more prevalent than malignant ones, which can lead to overly cautious algorithms and incorrect results. As a solution, researchers are developing computer-assisted diagnostic tools to detect malignant tumors early. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, the current YOLOv5 model is changed to obtain the desired outcomes by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The proposed approach improves melanoma detection with a real-time speed of 0.4 MS of non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for the precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to current melanoma detection approaches, the provided approach is more efficient in using deep features.

Classification of retinal diseases based on OCT Images.

Optical Coherence Topography (OCT) is an emerging biomedical imaging technology that offers non-invasive real-time, high-resolution imaging of highly scattering tissues. It is widely used in ophthalmology to perform diagnostic imaging on the structure of the anterior eye and the retina. Clinical studies are carried out to assess the application of OCT for some retinal diseases. OCT can provide means for early detection for various types of diseases because morphological changes often occur before the physical symptoms of these diseases. In addition, follow-up imaging can assess treatment effectiveness and recurrence of a disease. A review in this area is needed to identify the results and the findings from OCT images in the field of retinal diseases and how to use these findings to help in clinical applications. This paper overviews the current techniques that are developed to determine the ability of OCT images for early detection/diagnosis of retinal diseases. Also, the paper remarks several challenges that face the researchers in the analysis of the OCT retinal images.

Medical imaging diagnosis of early Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that influences the central nervous system, often leading to dire consequences for quality of life. The disease goes through some stages mainly divided into early, moderate, and severe. Among them, the early stage is the most important as medical intervention has the potential to alter the natural progression of the condition. In practice, the early diagnosis is a challenge since the neurodegenerative changes can precede the onset of clinical symptoms by 10-15 years. This factor along with other known and unknown ones, hinder the ability for the early diagnosis and treatment of AD. Numerous research efforts have been proposed to address the complex characteristics of AD exploiting various tests including brain imaging that is massively utilized due to its powerful features. This paper aims to highlight our present knowledge on the clinical and computer-based attempts at early diagnosis of AD. We concluded that the door is still open for further research especially with the rapid advances in scanning and computer-based technologies.

A Novel Early Diagnosis System for Mild Cognitive Impairment Based on Local Region Analysis: A Pilot Study.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that accounts for 60-70% of cases of dementia in the elderly. An early diagnosis of AD is usually hampered for many reasons including the variable clinical and pathological features exhibited among affected individuals. This paper presents a computer-aided diagnosis (CAD) system with the primary goal of improving the accuracy, specificity, and sensitivity of diagnosis. In this system, PiB-PET scans, which were obtained from the ADNI database, underwent five essential stages. First, the scans were standardized and de-noised. Second, an Automated Anatomical Labeling (AAL) atlas was utilized to partition the brain into 116 regions or labels that served for local (region-based) diagnosis. Third, scale-invariant Laplacian of Gaussian (LoG) was used, per brain label, to detect the discriminant features. Fourth, the regions' features were analyzed using a general linear model in the form of a two-sample t-test. Fifth, the support vector machines (SVM) and their probabilistic variant (pSVM) were constructed to provide local, followed by global diagnosis. The system was evaluated on scans of normal control (NC) vs. mild cognitive impairment (MCI) (19 NC and 65 MCI scans). The proposed system showed superior accuracy, specificity, and sensitivity as compared to other related work.