The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

Breast cancer is a globally prevalent and potentially fatal illness affecting women. Timely identification of screening mammography may decrease the occurrence of incorrect positive results and enhance the rate of patient survival. Nevertheless, the density of breast tissue in mammograms can impact the precision and effectiveness of detecting breast cancer. This paper examines the existing body of research on the analysis of breast density in mammograms utilising advanced deep learning models, including convolutional neural networks (CNN), transfer learning (TL), and ensemble learning (EL). Additionally, it examines various datasets and evaluation measures employed in the investigations. The study demonstrates that deep learning models can attain exceptional accuracy in categorising breast density. However, they encounter obstacles such as limited data availability, intricate model structures, and difficulties in interpreting the results. The research asserts that categorising breast density is an essential undertaking in order to enhance the identification and survival rates of breast cancer. Further investigation is warranted to examine the most effective deep learning structures, data augmentation methods, and interpretable models for this undertaking.