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Objective: Diagnosis of cancer at the benign stage is crucial. Recently, pathologists have been using computer-aided diagnostics with machine learning to diagnose patients from medical images. The limitation of the medical image dataset is a challenge to obtain a robust model for cancer identification. This research was conducted to address this issue. The weight of the pre-trained model can be learned more efficiently with a limited amount of data. However, the training process is conducted by customizing the freeze rate so that feature extraction is preserved besides tuning some hyperparameters. Methods: Transfer learning is a technique that can handle data limitation issues in the medical field. Transfer learning will reuse pre-trained model for different or specific task. Choosing an optimum architecture and hyperparameters in machine learning is very important to improve model performance. In our experiment, we carried out a hyperparameter optimization of various deep learning architectures that classifies images containing healthy and cancer tissue. Result: The research concludes that CNN with architecture DenseNet121, freeze rate 75%, zero hidden layers on the classifier, learning rate 0.001, and optimizer RMSProp have the best performance with 98% accuracy and 19.5 s training time using NVIDIA A100 GPU accelerator. Testing with the real dataset for future direction will be an achievement for the model’s success. Conclusion: This research has successfully optimized the Densnet121 deep learning architecture by tuning parameters. With a harmonic means value of 0.98, DenseNet121 outperforms the others. Compared to Camelyon, ImageNet still becomes the baseline of the transfer learning dataset because it has a rich amount of data.