Abstract:
In this paper, I explored the task of categorizing medical images into three categories:
benign, malignant, and normal. The dataset comprises images which are preprocessed and
resized for model training. After preprocessing, the dataset consists of 877 training samples
and 220 testing samples. Each image is represented as a grayscale image with a single
channel. The labels for the images are provided as one-hot encoded vectors, with each label
indicating the class of the corresponding image. The medical images used in this study are
sourced from a curated dataset specifically designed for research in medical imaging
analysis. This dataset contains a diverse range of images capturing various medical
conditions, allowing for comprehensive training and evaluation of the classification model.
The primary objective of this research is to develop a classification model capable of
accurately distinguishing between the three classes of medical images. To achieve this, I
plan to employ convolutional neural network (CNN) architectures, which have
demonstrated strong performance in image classification tasks. By leveraging CNNs, I aim
to capture relevant features from the medical images and utilize them for effective
classification. The evaluation of the classification model will be conducted using the testing
dataset, where the model's performance will be assessed based on metrics such as accuracy
precision, recall, and F1-score. Additionally, the model's generalization capability will be
analyzed to ensure its effectiveness in classifying unseen data. The outcome of thisresearch
holds significant implications for medical diagnostics and healthcare applications.
Accurate classification of medical images can aid healthcare professionals in identifying
and diagnosing various medical conditions, potentially leading to timely interventions and
improved patient outcomes. Furthermore, the developed classification model can serve as
a valuable tool for automated image analysis, augmenting the capabilities of medical
practitioners and enhancing the efficiency of diagnostic processes.
