Key Deep Learning Techniques for Disease Diagnosis

September 10, 2021 by Ajitesh Kumar · Leave a comment
disease diagnosis using machine learning
  • The disease diagnosis process has been the same for decades- a physician would analyze symptoms, perform lab tests, and refer to medical diagnostic guidelines. However, recent advances in AI/machine learning / deep learning have made it possible for computers to diagnose or detect diseases with human accuracy. This blog post will introduce some machine learning / deep learning techniques that can be used by data scientists for training models related to disease diagnosis.

What are different types of diseases that can be diagnosed using AI-based techniques?

The following is a list of different types of diseases that can be diagnosed using machine learning or deep learning-based techniques:

  • Cancer prognosis and detection: Different kinds of cancers can be diagnosed using machine learning algorithms. For example, deep learning and convolutional neural networks can be used to diagnose melanoma in images of skin lesions. Image processing, semantic segmentation, or deep learning-based machine learning algorithms can be applied in order to predict the presence of cancer tumors at an early stage as well as disease prognosis. Some other examples include deep neural networks that are trained on x-ray and mammogram images.
  • Heart diseases: Data scientists can make use of computer vision techniques to diagnose heart disease. For example, coronary artery disease (CAD) is a condition where plaque builds up in the blood vessels that supply oxygen-rich blood to the heart and brain. In order to visualize CAD using images taken from medical scans such as CT or MRI machines, data scientists can train a convolutional neural network to perform disease detection.
  • Liver diseases: AI-related techniques such as computer vision and machine learning algorithms have been used for early disease diagnosis of liver disease. For example, data scientists have trained deep neural networks to analyze ultrasound images in order to detect fatty liver disease or hepatic steatosis using disease markers such as liver texture or echogenicity.
  • Lungs diseases: Data scientists can use disease detection techniques on images of lungs in order to diagnose diseases such as tuberculosis, pulmonary nodules, and lung masses. For example, disease diagnoses involving CT-scanned x-ray images have been studied extensively by deep learning researchers for disease detection using features obtained from the image itself.
  • Autistic disorder: Autism spectrum disorder can be diagnosed using techniques such as neuroimaging and machine learning. Computer vision techniques such as face detection, facial feature analysis, or eye tracking can be used to detect the autistic disorder in children by analyzing their eye movements.
  • Diabetic retinopathy disease diagnosis: An example of this disease is when high blood sugar levels damage the small blood vessels in the retina. AI can be used to diagnose this disease by using computer vision techniques such as image segmentation, disease classification, or deep learning-based machine learning algorithms (e.g., convolutional neural networks) for disease detection and disease prognosis.
  • Psoriasis disease diagnosis: Deep learning models can be used to develop disease diagnostic algorithms for psoriasis disease based on images of the skin lesions caused by this disease.
  • Alzheimer’s disease: AI can be used to diagnose Alzheimer’s disease by extracting features from natural speech and then applying machine learning algorithms such as deep neural networks or support vector machines (SVM).
  • Parkinson’s disease: Deep learning algorithms have been used to diagnose Parkinson’s disease by taking into account movement-related features of the human body. For example, convolutional neural networks can be trained on video data in order to predict disease progression and monitor signs of stress or depression that are common symptoms of Parkinson’s disease.

What are some deep learning techniques for disease diagnosis/detection?

The following is a list of deep learning algorithms/techniques which can be used to train models for disease diagnosis:

  • Convolutional neural network (CNN): A deep learning technique that can be used for disease diagnosis. It is a type of artificial neural network which consists of at least three layers: convolutional, pooling, and fully connected layers. CNN can be used for disease diagnosis by building a disease-specific feature extraction model. It can also be used for disease prediction and drug discovery from medical images. Examples of diseases diagnosis problems using CNN include disease classification, disease segmentation, and disease identification. CNN uses images for disease diagnosis. Examples of diseases that CNNs can be used for includes skin cancer, breast cancer, and heart disease.
  • Fully Convolutional Networks (FCN): A deep learning technique that can be used for disease diagnosis with textual data such as patient’s medical records in electronic health record systems. It is a type of convolutional neural network which does not require segmentation and can be directly applied to disease diagnosis problems.
  • Recurrent Neural Network (RNN) / LSTM: A deep learning technique that is widely used in language processing applications such as machine translation models. It consists of at least two stacks of Long Short-Term Memory units (LSTMs), which are recurrent neural network cells. RNN can be used for disease diagnosis by building disease-specific language models, such as disease symptoms or medical records of the patients. Examples of diseases that RNNs can be used to build disease prediction models include Alzheimer’s disease, Parkinson’s Disease, and Crohn’s disease among others.
  • Dilated Convolutions: A deep learning technique that can be used for disease diagnosis. It is a type of convolutional neural network in which each weight matrix has an adjustable size (dilation). Dilated ConvNets are widely used methods in computer vision and image processing applications, such as object detection or segmentation problems. Examples of disease diagnosis problems using Dilated Convolutions include disease classification and disease segmentation.
  • Generative Adversarial Networks (GANs): A deep learning technique that can be used for disease detection as well as prediction of the onset of a disease. GAN is composed of two neural networks: one which generates samples and another, known as the discriminator, which evaluates them. GAN can be used for disease detection using medical images such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) or X-ray. For disease prediction problems, GAN uses disease symptoms and patient’s medical records to build a predictive model of the onset of disease. Examples of diseases that GAN can be used to build disease prediction models for include leukemia and myocardial infarction (heart disease).
  • Auxiliary Classifier GANs (AC-GAN): A deep learning technique that can be used for disease diagnosis. It is a type of adversarial network which consists of two adversaries: generative and discriminative networks, i.e., generator and critic networks. AC-GAN uses knowledge obtained from both models to produce more accurate results than GANs. AC-GAN can be used for disease prediction by building disease-specific feature extraction models using disease images, disease symptoms or medical records.
  • Convolutional Auxiliary Classifier Generative Adversarial Network (CAC-GAN): A deep learning technique that is suitable for multi-label classification problems such as disease diagnosis and disease prediction. CAC-GAN is a combination of generator networks that produce disease images, disease symptoms, or medical records and classifier networks for disease diagnosis.
  • Attention-based deep neural network (Attentional NN): A deep learning technique that can be used for disease diagnosis. It is a type of neural network which uses an attention mechanism to incorporate the context of input data into its output prediction, making it suitable for complex tasks such as disease diagnostics and treatment planning.
  • Adversarial autoencoders (AAE): Adversarial autoencoders (AAE) is a deep learning technique that can be used to build disease-specific feature extraction models. It consists of an encoder and a decoder, where the former transforms disease images or disease symptoms into disease-relevant features while the latter performs the reverse process from these features back to disease images or disease symptoms.
  • GANs with auxiliary output (GAN-AO): A deep learning technique that can be used for disease diagnosis. It is a type of discriminative adversarial network which uses an additional classifier network to classify the disease status of patients using disease images, i.e., malignant vs benign tumors in lung cancer cases or healthy vs diseased kidneys in diabetes cases.

In this blog post, we’ve introduced a few deep learning techniques that can be used for disease diagnosis. From Dilated ConvNets and Generative Adversarial Networks to Attention-based neural networks and Auxiliary Classifier GANs, the potential applications of these AI/deep learning tools and techniques are vast. Are you interested in knowing more about disease prediction or disease detection or diagnosis? If so, let us know more about what you need help with and we will reach out appropriately! We look forward to hearing from you soon.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com
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