Body: <p>I am a computer science student currently working on my final project, which involves finding a classification-based solution for predicting the head size of a fetus during its third month. Here is a reference for a regression solution utilizing image segmentation with the U-Net model, as illustrated in the <a href="https://github.com/udhayan47/Fetal-Head-Circumference-Prediction/tree/main" rel="nofollow noreferrer">Fetal-Head-Circumference-Prediction</a> project.</p>
  <p>Unlike that solution, I aim to use classification to detect if the head is of proper size.</p>
  <p><strong>My question is</strong> what approach is best to split a data set of gray images of fetal heads to classify if one is in abnormal size or not?</p>
  <p><a href="https://zenodo.org/records/1327317" rel="nofollow noreferrer">Here is a sample database for reference.</a></p>
  <p>My initial thought was to organize the images into age groups, similar to the list at the end of the FetalCns_Final.ipynb file provided in the first link. However, I am uncertain if this is the most suitable approach when dealing with similar images to classify age.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-01-18T11:07:43.767Z
Comments.Text: Hi and welcome to our community! I don't see any **specific question** being asked in your post. Could you please edit your post to explicitly ask a specific question and also put it in the title? If you have more than one question (which seems to be the case), ask the others in separate posts. Note that "any advice or recommendations" is not a question or at least not specific enough. Thanks.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-01-18T13:02:11.747Z
Comments.Text: @nbro, I clarified the explanation regarding the question
Comments.UserDisplayName: NitaStack
Tags: <convolutional-neural-networks><classification><image-recognition><pytorch>
Title: How to detect abnormal fetal head size with image classification?
UserDisplayName: NitaStack
ThreadText: # How to detect abnormal fetal head size with image classification?
  
  (Asked by: username_0 on 2024-01-18)
  
  I am a computer science student currently working on my final project, which involves finding a classification-based solution for predicting the head size of a fetus during its third month. Here is a reference for a regression solution utilizing image segmentation with the U-Net model, as illustrated in the [Fetal-Head-Circumference-Prediction](https://github.com/udhayan47/Fetal-Head-Circumference-Prediction/tree/main) project.
  
  Unlike that solution, I aim to use classification to detect if the head is of proper size.
  
  **My question is** what approach is best to split a data set of gray images of fetal heads to classify if one is in abnormal size or not?
  
  [Here is a sample database for reference.](https://zenodo.org/records/1327317)
  
  My initial thought was to organize the images into age groups, similar to the list at the end of the FetalCns\_Final.ipynb file provided in the first link. However, I am uncertain if this is the most suitable approach when dealing with similar images to classify age.
  
  **Tags:** convolutional-neural-networks, classification, image-recognition, pytorch
  > **Comments:**
  > **username_1**: Hi and welcome to our community! I don't see any **specific question** being asked in your post. Could you please edit your post to explicitly ask a specific question and also put it in the title? If you have more than one question (which seems to be the case), ask the others in separate posts. Note that "any advice or recommendations" is not a question or at least not specific enough. Thanks.  
  > **username_0**: @nbro, I clarified the explanation regarding the question  
  
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  ## 0 answers
  
  