1. Introduction to artificial intelligence techniques for medical image analysis ABDULHAMIT SUBASI 2. Lung cancer detection from histopathological lung tissue images using deep learning AAYUSH RAJPUT AND ABDULHAMIT SUBASI 3. Magnetic resonance imagining-based automated brain tumor detection using deep learning techniques ABHRANTA PANIGRAHI AND ABDULHAMIT SUBASI 4. Breast cancer detection from mammograms using artificial intelligence ABDULHAMIT SUBASI, AAYUSH DINESH KANDPAL, KOLLA ANANT RAJ AND ULAS BAGCI 5. Breast tumor detection in ultrasound images using artificial intelligence OMKAR MODI AND ABDULHAMIT SUBASI 6. Artificial intelligence-based skin cancer diagnosis ABDULHAMIT SUBASI AND SAQIB AHMED QURESHI 7. Brain stroke detection from computed tomography images using deep learning algorithms AYKUT DIKER, ABDULLAH ELEN AND ABDULHAMIT SUBASI 8. A deep learning approach for COVID-19 detection from computed tomography scans ASHUTOSH VARSHNEY AND ABDULHAMIT SUBASI 9. Detection and classification of Diabetic Retinopathy Lesions using deep learning SIDDHESH SHELKE AND ABDULHAMIT SUBASI 10. Automated detection of colon cancer using deep learning AAYUSH RAJPUT AND ABDULHAMIT SUBASI 11. Brain hemorrhage detection using computed tomography images and deep learning ABDULLAH ELEN, AYKUT DIKER AND ABDULHAMIT SUBASI 12. Artificial intelligence-based retinal disease classification using optical coherence tomography images SOHAN PATNAIK AND ABDULHAMIT SUBASI 13. Diagnosis of breast cancer from histopathological images with deep learning architectures EMRAH HANCER AND ABDULHAMIT SUBASI 14. Artificial intelligence based Alzheimer’s disease detection using deep feature extraction MANAV NITIN KAPADNIS, ABHIJIT BHATTACHARYYA AND ABDULHAMIT SUBASI

Discusses new deep learning algorithms for image analysis and how they are used for medical images Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes