𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭 𝐨𝐟 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠

 𝐓𝐨𝐩𝐢𝐜 - 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭 𝐨𝐟 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠

𝐀𝐫𝐭𝐢𝐜𝐥𝐞 𝐖𝐫𝐢𝐭𝐭𝐞𝐧 𝐁𝐲 - 𝐀𝐧𝐠𝐨𝐧𝐚 𝐁𝐢𝐬𝐰𝐚𝐬

𝐂𝐨𝐮𝐧𝐭𝐫𝐲 - 𝐁𝐚𝐧𝐠𝐥𝐚𝐝𝐞𝐬𝐡

The Healthcare sector is one of the supreme sectors for any country which plays a vital role. For any country, healthcare furtherance is very much essential. On the other hand, the fast evolution of technology is gazed. The involvement of technology can make this achievement of target more trouble-free. Disease diagnosis, cancer or tumor type detection, medicine production, health record maintaining and treatment instrumentations - all are now completely dependable on technology. Recently, Deep Learning is playing a dominant role on this view. This trending technology is showing several upgraded performances in the medical research sector. The main theme of this writing is to manifest the importance of deep learning in the medical sector from different perspectives.

The concept of deep learning was developed in 1943. It works based on Machine Learning method where the process of deep learning learns a machine for further prediction. Deep neural network concept is similar to the biological neuron concept. DL algorithm learns from input data and then reveals the outcome. This characteristic is utilized in medical science. Before any treatment, diagnosis and detection is very important for patient. Medical imaging like MRI, X-ray, CT scan and others can be diagnosed with the help of DL with better precision than manual way. Brain tumor, brain cancer, liver cancer, breast cancer, breast tumor are life-threatening diseases. Deep learning is capable of classify the cancer or tumor types with better accuracy and without wasting time. Before using DL algorithms, images are preprocessed and then features are extracted from the image. DL network has multiple layers and image is passed through those and layers extracts the features like filter. Finally, it goes for classification stage. Different research works are going on based on normal, pneumonia and COVID19 X-ray classification from X-ray dataset. Basically, DL algorithm nothing but neural network. Major two types of deep neural networks can be explained; one is Convolutional neural network and the another one is Recurrent Neural Network. But LSTM, GAN, RBFN, DBN are also known as predictor algorithms. There are some also well-known pre-trained DNN which are already proved with a large dataset. These are also used largely now for classification. Better prediction of imaging is helpful for patient’s treatment.

Drug invention, medical data analysis, classifying the patients by previous health records are also done by the use of DL algorithm now. It is not only minimized the time but also an accurate and efficient process. AI already proved that for drug invention it has a high success rate. It difficult to find or analyze a large amount of data but DL is actually can do this job easily. Successful implementation of deep learning is the concern of the commercial area now. A different commercial organization is now showing interest to use of deep learning for the betterment of medical science. Deep learning is a part of Artificial Intelligence (AI); it’s intelligence system can play a remarkable role for patient monitoring. After the outbreak of the pandemic in different universities, researchers started working to prevent the effect of COVID19. Different COVID 19 detection methodology using text data and image data are manifested some successful result for the future. The use of deep learning can also be seen at the clinical trial. Data dealing, feature mapping can easily be done by DL.

Overall, it comes to the conclusion that deep learning is changing the manual medical diagnosis or treatment view day-by-day. Our world is shifting from manual process to technological intelligence. The more accurate use of technology in the medical sector is more advancement can be predicted. Deep learning is helpful for various deadly disease treatment. If the recent technological improvement is continued then some amazing upgradation of the medical section is knocking at the future door.