EfficientNet Algorithm for Classification of Different Types of Cancer

Accurate and efficient classification of different types of cancer is critical for early detection and effective treatment. In this paper, we present the results of our experiments using the EfficientNet algorithm for classification of brain tumor, breast cancer mammography, chest cancer, and skin cancer. We used publicly available datasets and preprocessed the images to ensure consistency and comparability. Our experiments show that the EfficientNet algorithm achieved high accuracy, precision, recall, and F1 scores on each of the cancer datasets, outperforming other state-of-the-art algorithms in the literature. We also discuss the strengths and weaknesses of the EfficientNet algorithm and its potential applications in clinical practice. Our results suggest that the EfficientNet algorithm is well-suited for classification of different types of cancer and can be used to improve the accuracy and efficiency of cancer diagnosis.


Introduction
Cancer is a major cause of mortality worldwide, and early detection and accurate classification of different types of cancer is critical for effective treatment. Medical image analysis has become an important tool for the diagnosis and treatment of cancer, and recent advances in deep learning algorithms have shown promising results in this area. One such algorithm is the Efficient-Net, which has been shown to achieve state-of-the-art performance in various image classification tasks.
In this paper, we investigate the use of the Ef-ficientNet algorithm for the classification of different types of cancer, including brain tumor, breast cancer mammography, chest cancer, and skin cancer. We use publicly available datasets and preprocessed the images to ensure consistency and comparability. Our experi- breast cancer, which is the most common cancer among women worldwide. Chest cancer, including lung cancer, is a leading cause of cancer deaths worldwide. Skin cancer, including melanoma, is a rapidly growing cancer type with a high mortality rate if not detected and treated early.
Accurate classification of these types of cancer is crucial for effective treatment and patient outcomes.
Through our study, we aimed to investigate the effectiveness of the EfficientNet algorithm in classifying these types of cancer and contributing to the development of more accurate and efficient diagnostic tools.
In this paper, we implement the EfficientNet algorithm for the classification of different types of cancer, including brain tumor, breast cancer mammography, chest cancer, and skin cancer. We preprocessed the images to ensure consistency and comparability and trained the algorithm on publicly available datasets.
Our experiments demonstrate that the EfficientNet algorithm achieves high accuracy, precision, recall, and F1 scores on each of the cancer datasets, outperforming other state-of-the-art algorithms in the literature.
The rest of the paper is organized as follows. Section 2 describes our experimental methodology, including the datasets used and the details of the EfficientNet implementation. Section 3 presents the results of our experiments and compares them to other state-of-theart algorithms in the literature. Section 4 discusses the strengths and weaknesses of the EfficientNet algorithm and its potential applications in clinical practice.
Finally, Section 5 provides a summary of our findings and future research directions.
Samples Images of Cancers. By using these datasets and split sizes, the authors were able to train and test the EfficientNet algorithm for cancer classification, and obtain the performance metrics reported in their study.

Preprocessing methods
In order to ensure that the medical images used in this study were suitable for classification using the EfficientNet algorithm, several preprocessing steps were applied. These included: 1-Rescaling: All images were rescaled to a common size, typically 224x224 pixels, to ensure that they could be processed efficiently by the EfficientNet algorithm. 1. After the initial set of convolutional layers and max pooling, the output has a spatial dimension of 56x56x128.

2-Normalization
2. After the first EfficientNet-B0 block, the output has a spatial dimension of 28x28x40.
3. After the second EfficientNet-B0 block, the output has a spatial dimension of 14x14x72.
4. After the third EfficientNet-B0 block, the output has a spatial dimension of 7x7x120.

5.
After the global average pooling layer, the output has a dimension of 1x120.

Performance metrics for each cancer dataset
For each of the cancer datasets (brain tumor, breast cancer mammography, chest cancer, and skin cancer), we computed several performance metrics to evaluate the performance of the EfficientNet algorithm. These   Overall, the findings of this study suggest that the EfficientNet algorithm has significant potential for improving cancer diagnosis and treatment outcomes.
Further research is needed to explore the algorithm's potential in other types of cancer and to optimize its performance for clinical use.

Suggestions for future work
There are several areas where future research could expand on the findings of this study. Some potential directions for future work include: