Cattle external diseases like Foot and Mouth Disease (FMD), Lumpy Skin Disease (LSD), and Infectious Bovine Keratoconjunctivitis (IBK) are the most highly contagious diseases around the world. Early diagnosis is crucial for controlling these diseases. Traditional Convolutional Neural Networks is the most used architecture in the state-of-the-art of image processing and computer vision field. According to our knowledge, no other system for cattle disease detection in the husbandry farm has been introduced by using deep learning techniques. This proposed model referred to early detect the most common external diseases using several CNN architectures like conventional deep CNN, Inception-V3, and VGG-16 in the field of deep learning. All necessary steps for performing the diseases detection model are completely described in the paper, from the data collection to the process and outcome. The proposed system is established to be effective, acquiring results with 95% accuracy, which may reduce human errors in the identification process and will be helpful to recognize diseases for veterinarians and husbandry farmers.