*4.2. Medical Image Detection*

Image detection in healthcare refers to the process of detecting and identifying medical conditions or abnormalities in medical images such as X-rays, CT scans, and MRI scans. Image detection plays a vital role in the diagnosis and treatment of various medical conditions [43]. The following are the state-of-the-art techniques used in image detection in healthcare.

• Convolutional Neural Networks (CNNs): In medical imaging, CNNs have been used for a variety of applications, such as the detection of breast cancer, lung cancer, and brain tumors [44]. For example, in breast cancer detection, CNNs have been used to analyze mammograms and detect subtle changes that could indicate the presence of cancer. In lung cancer detection, CNNs have been used to analyze CT scans and identify nodules that could be indicative of cancer. In brain tumor detection, CNNs have been used to analyze MRI scans and identify regions of abnormal tissue growth [45].

One of the advantages of using CNNs for medical image detection is their ability to learn and extract features automatically, without the need for manual feature extraction [46]. This makes them particularly useful for analyzing large and complex medical images, where manual feature extraction can be time-consuming and prone to error. Another advantage of CNNs is their ability to learn from large amounts of data. With the increasing availability of medical imaging data, CNNs can be trained on large datasets to improve their accuracy and generalization performance [47]. Additionally, CNNs can be fine-tuned and adapted for specific medical image detection tasks, which can further improve their performance.

• Transfer Learning: In the context of medical image detection, transfer learning was an effective method for improving the accuracy and efficiency of image classification tasks [48]. Pre-trained models, such as those based on CNNs, can learn generic image features that can be transferred to new medical imaging datasets, even when the size of the new dataset is relatively small [49]. This can be particularly useful in healthcare, where obtaining large labeled datasets can be challenging and time-consuming. By using transfer learning, researchers and clinicians leveraged the knowledge and expertise gained from pre-trained models to improve the accuracy and efficiency of image detection in healthcare [50]. For example, a pre-trained model that was trained on a large dataset of chest X-rays was then fine-tuned for a smaller dataset of lung cancer images, resulting in improved accuracy and faster training times.
