Application of Computational Vision in the Medicine: Diagnosis of Pulmonary Diseases By means of X-ray Images with AI Artificial Intelligence (AI), in particular the Machine Learning (ML) and Deep Learning (DL), Is promoting significant revolutions in the medical field. Some of these applications include: Analysis and interpretation of medical images. Discovery of new drugs. Research focused on the genome. And many other applications. Deep Learning, for example, has demonstrated a superior ability in detecting and classifying diseases from medical images. As an illustrative study, an AI system developed by researchers from Germany, France and the United States surpassed dermatologists experienced in the detection of skin cancer. Objective of the work: Develop an AI model using Python on Google Colab that assists in the diagnosis of lung diseases through x-ray images. With this, we aim to reduce diagnostic time, minimize costs and, most crucially, save lives. Data: 133 x-ray images were collected, classified into: The images are available as an attachment, distributed in training and testing, and each class within a folder. 0,1,2,3 according to: 0. Covid 1. Normal exam 2. Viral Pneumonia 3. Bacterial Pneumonia Note: Even though Covid-19 is a viral disease, it is requested to differentiate between Covid-19 and other viral pneumonias due to professional requests. The images can be founded at the internet. Model Development: Using Deep Learning techniques, develop an Artificial Neural Network model (RNA) to classify the images. Choose and adjust the layers according to your judgment. Model Evaluation: Print the "classification report" to evaluate Metrics such as: precision, recall, f1-score, support And accuracy. Display the confusion matrix to view the Model performance in each class. The model needs to have a higher accuracy 0.55. Create a function that allows you to insert a New image and return the prediction of the model. Application of Computational Vision in the Medicine: Diagnosis of Pulmonary Diseases By means of X-ray Images with AI Artificial Intelligence (AI), in particular the Machine Learning (ML) and Deep Learning (DL), Is promoting significant revolutions in the medical field. Some of these applications include: Analysis and interpretation of medical images. Discovery of new drugs. Research focused on the genome. And many other applications. Deep Learning, for example, has demonstrated a superior ability in detecting and classifying diseases from medical images. As an illustrative study, an AI system developed by researchers from Germany, France and the United States surpassed dermatologists experienced in the detection of skin cancer. Objective of the work: Develop an AI model using Python on Google Colab that assists in the diagnosis of lung diseases through x-ray images. With this, we aim to reduce diagnostic time, minimize costs and, most crucially, save lives. Data: 133 x-ray images were collected, classified into: The images.

1. Application of Computational Vision in the
Medicine: Diagnosis of Pulmonary Diseases By means of X-ray Images with AI
Artificial Intelligence (AI), in particular the Machine Learning (ML) and Deep Learning (DL), Is
promoting significant revolutions in the medical field. Some of these applications include:
Analysis and interpretation of medical images.
Discovery of new drugs.
Research focused on the genome.
And many other applications.
Deep Learning, for example, has demonstrated a superior ability in detecting and classifying
diseases from medical images. As an illustrative study, an AI system developed by researchers
from Germany, France and the United States surpassed dermatologists experienced in the
detection of skin cancer.
Objective of the work:
Develop an AI model using Python on Google Colab that assists in the diagnosis of lung diseases
through x-ray images. With this, we aim to reduce diagnostic time, minimize costs and, most
crucially, save lives.

2. Data:
133 x-ray images were collected, classified into:
The images are available as an attachment, distributed in training and testing, and each class
within a folder. 0,1,2,3 according to:
0. Covid
1. Normal exam
2. Viral Pneumonia
3. Bacterial Pneumonia
Note: Even though Covid-19 is a viral disease, it is requested to differentiate between Covid-19
and other viral pneumonias due to professional requests.
The images can be founded at the internet.
Model Development:
Using Deep Learning techniques, develop an Artificial Neural Network model (RNA) to classify
the images. Choose and adjust the layers according to your judgment.
Model Evaluation:

3. Print the "classification report" to evaluate
Metrics such as: precision, recall, f1-score, support And accuracy.
Display the confusion matrix to view the Model performance in each class.
The model needs to have a higher accuracy 0.55.
Create a function that allows you to insert a
New image and return the prediction of the model.
Application of Computational Vision in the
Medicine: Diagnosis of Pulmonary Diseases By means of X-ray Images with AI
Artificial Intelligence (AI), in particular the Machine Learning (ML) and Deep Learning (DL), Is
promoting significant revolutions in the medical field. Some of these applications include:
Analysis and interpretation of medical images.
Discovery of new drugs.
Research focused on the genome.
And many other applications.
Deep Learning, for example, has demonstrated a superior ability in detecting and classifying
diseases from medical images. As an illustrative study, an AI system developed by researchers
from Germany, France and the United States surpassed dermatologists experienced in the

4. detection of skin cancer.
Objective of the work:
Develop an AI model using Python on Google Colab that assists in the diagnosis of lung diseases
through x-ray images. With this, we aim to reduce diagnostic time, minimize costs and, most
crucially, save lives.
Data:
133 x-ray images were collected, classified into:
The images are available as an attachment, distributed in training and testing, and each class
within a folder. 0,1,2,3 according to:
0. Covid
1. Normal exam
2. Viral Pneumonia
3. Bacterial Pneumonia
Note: Even though Covid-19 is a viral disease, it is requested to differentiate between Covid-19
and other viral pneumonias due to professional requests.
The images can be founded at the internet.

5. Model Development:
Using Deep Learning techniques, develop an Artificial Neural Network model (RNA) to classify
the images. Choose and adjust the layers according to your judgment.
Model Evaluation:
Print the "classification report" to evaluate
Metrics such as: precision, recall, f1-score, support And accuracy.
Display the confusion matrix to view the Model performance in each class.
The model needs to have a higher accuracy 0.55.
Create a function that allows you to insert a
New image and return the prediction of the model.