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Go to Editorial ManagerCancer is a disease caused by uncontrollable cell growth and division. Surgery, chemotherapy, radiotherapy, and hormonotherapy are all cancer treatment options. In addition to noninvasive cancer ablative therapy. As an example, ultrasonic therapy, even with low-intensity pulsed ultrasound (LIPUS) or high-intensity focused ultrasound (HIFU), and Laser therapy (photo-biomodulation therapy) in low-level laser therapy (LLLT) with different wavelength ranges from ultraviolet (UV), visible and infrared (IR) that all have demonstrated different results depending on the target of treatment so previous trials therapies are being studied. This paper reviews recent studies on the in vitro treatment effect of ultrasound therapy and laser therapy on normal and cancerous cell lines with specific parameters. The effect of ultrasound results showed a decrease in cell proliferation and an increase in apoptosis in different types of cells, depending especially on sound intensity, known as Special Peak Temporal Average Intensity (ISPTA). While the laser effect is noticed on cell viability, either enhance or inhibit their viability depending upon the dose of exposure and other specific parameters like wavelength, energy density, and power density used in each treatment protocol. The previous studies conclude that each response would have a treatment method with specific parameters, even an increase or decrease in cell viability. Further studies need to be applying these methods in vivo.
Breast cancer is one of the greatest frequent tumours among females in Iraq. Medical ultrasound imaging has become a common modality for breast tumour imaging because of its ease of use, low cost, and safety. In the present study, Convolutional Neural Network (CNN) feature extraction approaches were used to classify breast ultrasound imaging. The CNN model used is composed of four-layer for breast cancer ultrasound image analysis. Two types of free datasets were used. These data were divided into groups A and B. Group A has three classes, namely benign, malignant and normal, while group B has two classes, namely, benign and malignant. The proposed technique was assessed based on its accuracy, precision, F1 score and recall. The model's classification accuracy for data A was 96%, whereas for data B was 100%.
Diabetic peripheral neuropathy represents one of the common long-terms complications that effect about fifty percentage?of diabetes patients. The habitual diagnosis tool based on nerve conduction study that examine the nerve damage and classify the patient status into normal and diabetic peripheral neuropathy with degree of severity without considering the effect on skeletal muscle and take on patient data. A complementary diagnostic tool proposed, in this study integrates the patient’s data including body mass index, age and duration of diabetic, average blood glucose levels, nerve conduction study that involves amplitude and latency of peroneal and tibial nerves and muscle ultrasound alongside the machine learning algorithms to facilitate the clinicians for a precise diagnosis. A group of control and diabetic patients utilized to gather the data with calculating the muscle thickness and statistical properties from the gray-level ultrasound images of six skeletal muscles. Support vector machine, naïve bayes, ensemble of bagged tree and artificial neural network supervised machine learning algorithms categorize each class with a high classification accuracy, 98.1% for tibialis anterior with naïve bayes algorithm. The outcomes of this study show a promising complementary diagnostic tool that will help the clinicians to perform an exact diagnosis and disclose the side effect on both nerves and muscles of diabetic patients.
Thyroid nodules (TNs) are discrete abnormalities located within the thyroid gland that are radiologically different from the surrounding thyroid tissue. Ultrasound is an accurate and efficient way to diagnose thyroid nodules. Recently, several methods of AI were proposed to improve the detection of thyroid nodules ultrasound images with good performances. However, in some cases related to the type or size of the dataset using machine or transfer deep learning methods alone is unable to achieve high accuracy and high specificity. Consequently, the addition of feature selection)FS) to the deep learning method enhances the results by reducing the high features and the time needed for training the dataset. This study proposes two deep-learning models for classifying thyroid nodule US images into two categories: benign and malignant. ResNet50 was the first model used to extract deep features from US images. The second model integrates ResNet50 and principal component analysis (PCA) for feature selection, intending to reduce dataset dimensionality while maintaining the greatest data variance possible before classification. The proposed model was created using a freely available dataset. The dataset consists of 800 images, 400 benign and 400 malignant. The suggested system was accessed based on accuracy, precision, recall, and F1 score. The classification accuracy for ResNet50 was 85%, while ReNet50-PCA was 89.16%. The combination of deep learning and FS techniques in this research produces an interesting diagnostic framework that can potentially increase efficiency and accuracy in thyroid cancer detection, especially in local healthcare centers.
Kidney disease is a global health concern, often leading to kidney failure and impaired function. Artificial intelligence and deep learning have been extensively researched, with numerous proposed models and methods to improve kidney disease diagnosis. This work aims to enhance the efficiency and accuracy of the diagnostic system for kidney disease by using Deep Learning, thereby contributing to effective healthcare delivery. This work proposed three models: CNN, CNN-XGBoost and CNN-RF to extract features and classify kidney Ultrasound images into four categories: three abnormal cases (stones, hydronephrosis, and cysts) and one normal case. The models were tested on a real dataset of 1260 kidney ultrasound images (from 1000 patients) collected from the Lithotripsy Centre in Iraq. CNN models are often viewed as black boxes due to the challenge of understanding their learned behaviors, Visualizing Intermediate Activations (VIA) was used to address this issue. The proposed framework was assessed based on precision, recall, F1-score, and accuracy. CNN-RF is the most accurate model, with an accuracy of 99.6%. This study can potentially assist radiologists in high-volume medical facilities and enhance the accuracy of the diagnostic system for kidney disease.