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The aim of the study is to detect the abnormal area(s) from brain CTs of stroke patients using Image Processing and to accurately evaluate the stroke changes in brain tissues among patients with Deep Learning models in MATLAB 2019b interface. 1000 patients (500 stroke suspected, 500 healthy participants) were chosen between 25 and 75 age ranges from TOBB ETU and Yıldırım Beyazıt University Hospitals according to the ethics committee certificate. For this study, for increasing the accuracy and eliminating the redundancy, from the image data of the patients, only lateral and 4th ventricle CT images were used. Firstly, these images were processed via Image Processing methods (Image Acquisition, Preprocessing, Thresholding, Segmentation, Morphological Operations etc.). After these methods, the resulted lateral ventricle image was split into 6 specific areas and 4th ventricle image was split into 14 specific areas like automated computerized Alberta Stroke Scoring, respectively. For 1000 images, totally 20x1000=20000 pieces of CT subimages were obtained with the specific class names (as healthy and stroke) and were used as the input of Artificial Intelligence (AI) and Deep Learning (DL) models (optimized ANN with Levenberg-Marquardt method and CNN). This approach can give an important chance to the doctors for supporting their results with a decision support system, speeding up the diagnosis time and also decreasing the possible rate of misdiagnosis.

For years, breast cancer has been a serious problem and malignant tumor case primarily causes death of women all around the world. In this paper, a computer based breast tumor analysis and pathological case classification system has been achieved and some novelties are included to the image processing methods, especially in segmentation and base frequency distribution acquisition of the processed image and classification part. First, the possible noises and artifacts are eliminated by using common filtering. Second, the filtered images are segmented with integrating gray level Image Processing methods. Then, these images (ROIs) are converted to the base frequency distribution images with using Fast Fourier Transform (FFT) and Lab&HSV color spaces. The most important key for these images is frequency distribution can be obtained with specific color tones and totally 100 images (50 benign-50 malignant) are accumulated to fed the two different Machine Learning models in literature such as Probabilistic Neural Network as Learning Vector Quantization (LVQ) and Support Vector Regression (SVR) for classification of Benign and Malignant cases without the need for additional medical data. Then the performance of the proposed system is analyzed with 30 different test images (15 benign-15 malignant) according to the metrics like accuracy, sensitivity, specificity, precision, F-score and area under the ROC curve (AUC score). The experimental results on the open access mammogram image set show that discriminating between Benign and Malignant cases can be achieved with an important success rate as 91.38% with LVQ and %.92 with SVR.

A sudden outbreak of Monkeypox disease has been reported recently in up to 70 countries so far and the spreading rate may be seen significantly around the world. The clinical aspects of Monkeypox have been reported that this disease looks like similar in many attributes when comparing some specific skin lesions as Chickenpox, Measles etc. These similarities make Monkeypox diagnosing and detecting difficult for doctors, clinicians or professionals by examining the visual appearance of the lesion on the skin. In addition, there has been a problem with the lack of detailed information about ultimate diagnosing of novel Monkeypox disease. It is also important that by the success of the studies about AI, Machine Learning and Deep Learning models in COVID-19 detection, the community has begun to give importance to detect Monkeypox via comprehensive AI methods from digital skin images. Moreover, in this paper, we develop a larger dataset to study and analyze the feasibility of common Artificial Intelligence based Deep Learning methods on skin images for Monkeypox detection. Our study has shown that Deep Learning models have a great and important success for detecting this disease from digital skin images via modifying/ updating some layers in the Transfer Learning. The other important information can be explained as because of being quite similar in some aspects to the other skin lesions and the lack of the detailed attributes/features of Monkeypox, detecting via specific AI models with Feature extraction process have become a bit difficult, unknown and time consuming in contrast to the Deep Learning models (AlexNet and VGG16 models in MATLAB software). The future aim is to develop a prototype web application and it is important that to improve the accuracy of Monkeypox detection, a larger demographically diverse dataset is required.

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